The Power of Swift for Machine Learning (TensorFlow Meets)

The Power of Swift for Machine Learning (TensorFlow Meets) [Collection] On this episode of TensorFlow Meets, Laurence (@lmoroney) talks with Chris Lattner (@clattner_llvm), the creator of Swift, about how Swift has grown beyond mobile development, and can now be used to train neural networks in TensorFlow. Get started with Swift for TensorFlow → http://bit.ly/2HxY4e4 A Swift Tour in Colab → http://bit.ly/2w6CqrM Swift on fast.ai → http://bit.ly/2JqT7ai This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish. Subscribe to the TensorFlow channel → http://bit.ly/TensorFlow1 Watch more episodes of TensorFlow Meets → http://bit.ly/2lbyLDK

ML & AI sandbox demos at Google I/O 2019

ML & AI sandbox demos at Google I/O 2019 [Collection] The TensorFlow team takes you inside the ML & AI sandbox at Google I/O 2019 to show you some of the coolest new demos powered by TensorFlow. Dance Like teaches people how to dance by using TensorFlow Lite to run multiple models in real-time on a mobile device. PoseNet and Piano Genie both use TensorFlow.js to run ML models entirely in the browser. To learn more about TensorFlow Lite and TensorFlow.js and get started, check out the links below! Try TF Lite here → https://www.tensorflow.org/lite TensorFlow.js → https://www.tensorflow.org/js/ Pose estimation with PoseNet → https://bit.ly/2VWVsjW Piano Genie web demo → http://piano-genie.glitch.me/ Libraries & extensions → https://bit.ly/2weLHOz Subscribe to the TensorFlow Channel → http://bit.ly/TensorFlow1

Inside TensorFlow: Summaries and TensorBoard

Inside TensorFlow: Summaries and TensorBoard [Collection] Take an inside look into the TensorFlow team’s own internal training sessions--technical deep dives into TensorFlow by the very people who are building it! This week we take a look into TensorBoard with Nick Felt, an Engineer on the TensorFlow team. Learn how TensorBoard and the tf.summary API work together to visualize your data, including details about API changes, log directories, event files, and best practices. Let us know what you think about this presentation in the comments below! Also, check out @Tensorboard in Twitter! TensorFlow's visualization toolkit → https://goo.gle/2LKGpVy TensorFlow on GitHub → https://goo.gle/2HpX3V5 Watch more from Inside TensorFlow Playlist → https://bit.ly/2JBXFtt Subscribe to the TensorFlow channel → https://bit.ly/TensorFlow1

TensorFlow Lite for on-device ML (TensorFlow Meets)

TensorFlow Lite for on-device ML (TensorFlow Meets) [Collection] TensorFlow Lite is an open source deep learning framework for on-device inference, allowing you to deploy machine learning models on mobile and IoT devices. On this episode of TensorFlow Meets, Laurence (@lmoroney) talks with TF Lite Engineering Lead Raziel Alvarez about how TensorFlow Lite aims to enable the next generation of AI-based applications. Raziel’s TF Lite talk from TF Dev Summit ‘19 → https://bit.ly/2Ja71gJ TensorFlow Lite examples → http://bit.ly/2PSC0OO This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish. Subscribe to the TensorFlow channel → https://bit.ly/TensorFlow1 Watch more episodes of TensorFlow Meets → https://bit.ly/2lbyLDK

TensorFlow 2.0 upgrade, Python support, & more! (#AskTensorFlow)

TensorFlow 2.0 upgrade, Python support, & more! (#AskTensorFlow) [Collection] In a special live episode from the TensorFlow Dev Summit, Paige (@DynamicWebPaige) and Laurence (@lmoroney) answer your #AskTensorFlow questions. Learn about TensorFlow prebuilt binaries, the TF 2.0 upgrade script, estimators and Keras in TensorFlow 2.0, and Python support roadmap. Remember to use #AskTensorFlow to have your questions answered in a future episode! Nvidia GPU-enabled system requirements → https://goo.gle/2H4GVt8 TensorFlow builds special interest group → https://goo.gle/2vHUubK Upgrading your code to TF 2.0 → https://goo.gle/2LqL3bl TensorFlow 2.0 project tracker → https://goo.gle/2JjAkNe This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish. Subscribe to the TensorFlow channel → http://bit.ly/TensorFlow1 Watch more episodes of #AskTensorFlow → http://bit.ly/2JcL3tT

Dance Like, an app that helps users learn how to dance using machine learning

Dance Like, an app that helps users learn how to dance using machine learning [Collection] At Google I/O '19, the TensorFlow Lite team demoed Dance Like, an app that helps users learn how to dance using machine learning. The experience slows down a real-time dance that the user then dances too, runs a body-part segmentation model, via TensorFlow Lite, on both the user (the student) and the subject in the video (the teacher) to create a matching score and returns that feedback to the user in realtime. Subscribe to the TensorFlow channel → https://bit.ly/TensorFlow1

Introducing Google Coral: Building On-Device AI (Google I/O'19)

Introducing Google Coral: Building On-Device AI (Google I/O'19) [Collection] This session will introduce you to Google Coral, a new platform for on-device AI application development and showcase it's machine learning acceleration power with TensorFlow demos. Coral offers the tools to bring private, fast, and efficient neural network acceleration right onto your device and enables you to grow ideas of AI application from prototype to production. You will also learn the technical specs of Edge TPU hardware and software tools, as well as application development process.  Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speake: Bill Luan T2BB62

A Fireside Chat with Turing Award Winner Geoffrey Hinton, Pioneer of Deep Learning (Google I/O'19)

A Fireside Chat with Turing Award Winner Geoffrey Hinton, Pioneer of Deep Learning (Google I/O'19) [Collection] In this rare interview since (jointly) winning the 2018 Turing Award for his work on neural networks, hear about the conceptual and engineering breakthroughs that have made deep neural networks a critical element of computing. Their research has allowed artificial intelligence technologies to progress at a rate that was not possible in the past and has reinvented the way technology is built. Watch more #io19 here: Inspiration at Google I/O 2019 Playlist → https://goo.gle/2LkBwCF TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Geoffrey Hinton, Nicholas Thompson TDAA69

Cutting Edge TensorFlow: New Techniques (Google I/O'19)

Cutting Edge TensorFlow: New Techniques (Google I/O'19) [Collection] There's lots of great new things available in TensorFlow since last year's IO. This session will take you through 4 of the hottest from Hyperparameter Tuning with Keras Tuner to Probabilistic Programming to being able to rank your data with learned ranking techniques and TF-Ranking. Finally, you will look at TF-Graphics that brings 3D functionalities to TensorFlow. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Elie Burzstein , Josh Dillon, Michael Bendersky, Sofien Bouaziz TDA482

TF-Agents: A Flexible Reinforcement Learning Library for TensorFlow (Google I/O'19)

TF-Agents: A Flexible Reinforcement Learning Library for TensorFlow (Google I/O'19) [Collection] TF-Agents is a clean, modular, and well-tested open-source library for Deep Reinforcement Learning with TensorFlow. This session will cover recent advancements in Deep RL, and show how TF-Agents can help to jump start your project. You will also see how TF-Agent library components can be mixed, matched, and extended to implement new RL algorithms. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Sergio Guadarrama and Eugene Brevdo TFA7A8

Cloud TPU Pods: AI Supercomputing for Large Machine Learning Problems (Google I/O'19)

Cloud TPU Pods: AI Supercomputing for Large Machine Learning Problems (Google I/O'19) [Collection] Cloud Tensor Processing Unit (TPU) is an ASIC designed by Google for neural network processing. TPUs feature a domain specific architecture designed specifically for accelerating TensorFlow training and prediction workloads and provides performance benefits on machine learning production use. Learn the technical details of Cloud TPU and Cloud TPU Pod and new features of TensorFlow that enables a large scale model parallelism for deep learning training. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Kaz Sato and Martin Gorner TF6510

Machine Learning Fairness: Lessons Learned (Google I/O'19)

Machine Learning Fairness: Lessons Learned (Google I/O'19) [Collection] ML fairness is a critical consideration in machine learning development. This session will present a few lessons Google has learned through our products and research and how developers can apply these learnings in their own efforts. Techniques and resources will be presented that enable evaluation and improvements to models, including open source datasets and tools such as TensorFlow Model Analysis. This session will enable developers to proactively think about fairness in product development. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Tulsee Doshi and Jacqueline Pan T8ACB1

Machine Learning Zero to Hero (Google I/O'19)

Machine Learning Zero to Hero (Google I/O'19) [Collection] This is a talk for people who know code, but who don’t necessarily know machine learning. Learn the ‘new’ paradigm of machine learning, and how models are an alternative implementation for some logic scenarios, as opposed to writing if/then rules and other code. This session will guide you through understanding many of the new concepts in machine learning that you might not be familiar with including eager mode, training loops, optimizers, and loss functions. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Laurence Moroney and Karmel Allison T700B4

Machine Learning for Game Developers (Google I/O'19)

Machine Learning for Game Developers (Google I/O'19) [Collection] Machine learning is enabling game developers to solve challenges that have been difficult with traditional programming techniques. If you're new to machine learning and looking to consume APIs backed by Google-built ML models or wanting to train your own game AI with a custom model, in this session, you'll learn about the many options Google provides for game developers. Watch more #io19 here: Gaming at Google I/O 2019 Playlist → https://goo.gle/300WsBY TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Ankur Kotwal TB2066

Federated Learning: Machine Learning on Decentralized Data (Google I/O'19)

Federated Learning: Machine Learning on Decentralized Data (Google I/O'19) [Collection] Meet federated learning: a technology for training and evaluating machine learning models across a fleet of devices (e.g. Android phones), orchestrated by a central server, without sensitive training data leaving any user's device. Learn how this privacy-preserving technology is deployed in production in Google products and how TensorFlow Federated can enable researchers and pioneers to simulate federated learning on their own datasets. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Daniel Ramage and Emily Glanz TDC839

Writing the Playbook for Fair & Ethical Artificial Intelligence & Machine Learning (Google I/O'19)

Writing the Playbook for Fair & Ethical Artificial Intelligence & Machine Learning (Google I/O'19) [Collection] Learn from Googlers who are working to ensure that a robust framework for ethical AI principles are in place, and that Google's products do not amplify or propagate unfair bias, stereotyping, or prejudice. Hear about the research they are doing to evolve artificial intelligence towards positive goals: from accountability in the ethical deployment of AI, to the tools needed to actually build them, and advocating for the inclusion of concepts such as race, gender, and justice to be considered as part of the process. Watch more #io19 here: Inspiration at Google I/O 2019 Playlist → https://goo.gle/2LkBwCF TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Jen Gennai, Margaret Mitchell, Jamila Smith-Loud TC3A01

Deep Learning to Solve Challenging Problems (Google I/O'19)

Deep Learning to Solve Challenging Problems (Google I/O'19) [Collection] This talk will highlight some of Google Brain’s research and computer systems with an eye toward how it can be used to solve challenging problems, and will relate them to the National Academy of Engineering's Grand Engineering Challenges for the 21st Century, including the use of machine learning for healthcare, robotics, and engineering the tools of scientific discovery. He will also cover how machine learning is transforming many aspects of our computing hardware and software systems. Watch more #io19 here: Inspiration at Google I/O 2019 Playlist → https://goo.gle/2LkBwCF TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker: Jeff Dean T0E51E

Machine Learning Magic for Your JavaScript Application (Google I/O'19)

Machine Learning Magic for Your JavaScript Application (Google I/O'19) [Collection] TensorFlow.js is a library for training and deploying machine learning models in the browser and in Node.js and offers unique opportunities for JavaScript developers. In this talk, you will learn about the TensorFlow.js ecosystem: how to bring an existing machine learning model into your JS app, re-train the model using your data and go beyond the browser to other JS platforms. Come see live demos of some of our favorite and unique applications! Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Yannick Assogba , Sandeep Gupta T440E5

TensorFlow Extended (TFX): Machine Learning Pipelines and Model Understanding (Google I/O'19)

TensorFlow Extended (TFX): Machine Learning Pipelines and Model Understanding (Google I/O'19) [Collection] This talk will focus on creating a production machine learning pipeline using TFX. Using TFX developers can implement machine learning pipelines capable of processing large datasets for both modeling and inference. In addition to data wrangling and feature engineering over large datasets, TFX enables detailed model analysis and versioning. The talk will focus on implementing a TFX pipeline and a discussion of current topics in model understanding. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Kevin Haas , Tulsee Doshi , Konstantinos Katsiapis T02F52

Swift for TensorFlow (Google I/O'19)

Swift for TensorFlow (Google I/O'19) [Collection] Swift for TensorFlow is a platform for the next generation of machine learning that leverages innovations like first-class differentiable programming to seamlessly integrate deep neural networks with traditional software development. In this session, learn how Swift for TensorFlow can make advanced machine learning research easier and why Jeremy Howard’s fast.ai has chosen it for the latest iteration of their deep learning course. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): James Bradbury and Richard Wei T88DD5

AI for Mobile and IoT Devices: TensorFlow Lite (Google I/O'19)

AI for Mobile and IoT Devices: TensorFlow Lite (Google I/O'19) [Collection] Imagine building an app that identifies products in real time with your camera or one that responds to voice commands instantly. In this session, you'll learn how to build AI into any device using TensorFlow Lite, and no ML experience is required. You’ll discover a library of pretrained models that are ready to use in your apps, or customize to your needs. You’ll see how quickly you can add ML to Android and iOS apps and learn about the future of on-device ML and our roadmap. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Sarah Sirajuddin and Tim Davis T76FCA

Getting Started with TensorFlow 2.0 (Google I/O'19)

Getting Started with TensorFlow 2.0 (Google I/O'19) [Collection] TensorFlow 2.0 is here! Understand new user-friendly APIs for beginners and experts through code examples to help you create different flavors of neural networks (Dense, Convolutional, and Recurrent) and understand when to use the Keras Sequential, Functional, and Subclassing APIs for your projects. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Josh Gordon, Paige Bailey TCDFE8

Machine Learning on Your Device: The Options (Google I/O'19)

Machine Learning on Your Device: The Options (Google I/O'19) [Collection] Developers have an often confusing plethora of options available to them in using machine learning to enhance their mobile apps and edge devices. This session will demystify these options, showing you how TensorFlow can be used to train models and how you can use these models across a variety of devices with TensorFlow Lite. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Laurence Moroney, Daniel Situnayake T6D370

(ML 1.3) What is unsupervised learning?

(ML 1.3) What is unsupervised learning? [Collection] A broad overview. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA

(ML 1.2) What is supervised learning?

(ML 1.2) What is supervised learning? [Collection] A broad overview. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA

(ML 1.1) Machine learning - overview and applications

(ML 1.1) Machine learning - overview and applications [Collection] Attempt at a definition, and some applications of machine learning. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA

Homemade Video Arcade Machine - Computerphile

Homemade Video Arcade Machine - Computerphile [Collection] Nottingham Hackspace member Michael Erskine has built an arcade machine to run his favourite game from his teens - Defender. http://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computerphile is a project by Brady Haran. See the full list of Brady's video projects at: http://periodicvideos.blogspot.com/20...

Welcome to Computerphile!

Welcome to Computerphile! [Collection] We want to know what you want to see on this channel - leave a comment below or contact us via social media to let us know. We're listening! http://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computerphile is a project by Brady Haran See the full list of Brady's video projects at: http://periodicvideos.blogspot.com/20...

ML & AI sandbox demos at Google I/O 2019

ML & AI sandbox demos at Google I/O 2019 [Collection] The TensorFlow team takes you inside the ML & AI sandbox at Google I/O 2019 to show you some of the coolest new demos powered by TensorFlow. Dance Like teaches people how to dance by using TensorFlow Lite to run multiple models in real-time on a mobile device. PoseNet and Piano Genie both use TensorFlow.js to run ML models entirely in the browser. To learn more about TensorFlow Lite and TensorFlow.js and get started, check out the links below! Try TF Lite here → https://www.tensorflow.org/lite TensorFlow.js → https://www.tensorflow.org/js/ Pose estimation with PoseNet → https://bit.ly/2VWVsjW Piano Genie web demo → http://piano-genie.glitch.me/ Libraries & extensions → https://bit.ly/2weLHOz Subscribe to the TensorFlow Channel → http://bit.ly/TensorFlow1

10 Ways to Learn Faster

10 Ways to Learn Faster I'm going to reveal 10 learning techniques that I personally use to educate myself on complex topics in Science, engineering, technology, and mathematics! These are techniques that I've used for years now, and each of them is backed by Scientific literature. I encourage you to implement them in your learning journey to see if they work for you. We are now living in the age of information and the possibilities to learn anything are truly endless. Thus, learning how to learn is one of the most important skills to have, regardless of your career. Enjoy! Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval The 10 techniques (lots more tips + details in the video!) #1 - Believe in your ability to learn #2 - Create a custom curriculum #3 - Avoid multitasking #4 - Meditate daily #5 - Constant cardio #6 - Dependency parsing #7 - Handwrite notes #8 - Teach others #9 - Eat well #10 - Sleep well Examples of my curriculums: https://github.com/llsourcell Bryan's article on sleep: https://bryanjohnson.co/newsletter/sleep-is-the-new-coffee/ More learning videos by me: https://www.youtube.com/watch?v=nxWfZP6eslM https://www.youtube.com/watch?v=YzfdL58virc&t=542s https://www.youtube.com/watch?v=waXHrc2m9K8 Make Money with Tensorflow 2.0: https://youtu.be/WS9Nckd2kq0 Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams! Join us at the School of AI: https://theschool.ai/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w And please support me on Patreon: https://www.patreon.com/user?u=3191693

Watch Me Build an Education Startup

Watch Me Build an Education Startup I've built a tool for teachers that automatically grades and validates essays using modified versions of popular language models, specifically BERT and GPT-2. It's called EssayBrain and I built it using the Python programming language, as well Flask, Tensorflow.js, Tensorflow, D3.js, CopyLeaks, Stripe, and Firebase. In this video tutorial, i'll guide you through my process as I build this project. The code is open source and I'll link to it below. Use it as inspiration to start your own profitable business in this space. We've got to upgrade education, and with the power of technology anyone anywhere can create a viable engineering solution that creates a positive impact. Enjoy! Code for this video: https://github.com/llSourcell/Watch-Me-Build-an-Education-Startup Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Watch Me Build a Marketing Startup: https://www.youtube.com/watch?v=6oM3N6PRFz8&t=825s Watch Me Build a Finance Startup: https://www.youtube.com/watch?v=oeraUtRgsbI&t=591s Make Money with Tensorflow 2.0: https://youtu.be/WS9Nckd2kq0 How to Make Money with Tensorflow: https://www.youtube.com/watch?v=HhqhFbwiaig&t=2s 7 Ways to Make Money with Machine Learning: https://www.youtube.com/watch?v=mrRfpiAwad0&t=200s Watch me Build an AI Startup: https://www.youtube.com/watch?v=NzmoPqte4V4&t=172s Intro to Tensorflow: https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams! Join us at the School of AI: https://theschool.ai/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w And please support me on Patreon: https://www.patreon.com/user?u=3191693

Learn Physics Fast

Learn Physics Fast I've compiled a 2 month Physics curriculum using free resources from across the Internet. Physics helped us build modern civilization. It's used extensively in computer engineering, quantum computing, and across many Scientific disciplines. Learning Physics helps hone your ability to think critically about the nature of reality, and this helps elevate your consciousness. In this video, I'll explain my curriculum and guide you through my process. Enjoy! Curriculum for this video: https://github.com/llSourcell/Learn_Physics_in_2_Months Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Edit * - i mispronounced Leonard, oops! Week 1 Math Review https://www.souravsengupta.com/cds2016/lectures/Savov_Notes.pdf https://www.youtube.com/watch?v=kjBOesZCoqc&index=1&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab http://tutorial.math.lamar.edu/pdf/Calculus_Cheat_Sheet_All.pdf https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr https://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf Week 2 Classical Mechanics Lectures https://www.youtube.com/watch?v=ApUFtLCrU90&list=PL47F408D36D4CF129 Study Guide http://www.maths.liv.ac.uk/TheorPhys/people/staff/jgracey/math228/formula.pdf Final Exam http://galileo.phys.virginia.edu/classes/321.jvn.fall02/Fin2002s.pdf Week 3 Statistical Mechanics Lectures https://www.youtube.com/watch?v=D1RzvXDXyqA&t=619s Study Guide https://pdfs.semanticscholar.org/a4d6/cd309dd005c4e30c8a4dbe3ed4c377de32ec.pdf Final Exam http://www.phys.ttu.edu/~cmyles/Phys5305/Exams/Phys5305%20Final%20Exam%20Spring2009.PDF Week 4 Electromagnetism Lectures https://www.youtube.com/watch?v=x1-SibwIPM4&list=PLyQSN7X0ro2314mKyUiOILaOC2hk6Pc3j&index=2 Study Guide http://www.phys.nthu.edu.tw/~thschang/notes/EM02.pdf Final Exam http://web.mit.edu/8.02/www/Spring02/exams/final-sol4.pdf Month 2 Week 5 Particle Physics Lectures https://www.coursera.org/learn/particle-physics Study Guide https://www.nikhef.nl/~i93/Master/PP1/2011/Lectures/Lecture.pdf Final Exam http://hitoshi.berkeley.edu/129A/final-sol.pdf Week 6 Theory of Relativity Lectures https://www.youtube.com/watch?v=JRZgW1YjCKk&list=PLXLSbKIMm0kh6XsMSCEMnM02kEoW_8x-f Study Guide https://arxiv.org/pdf/gr-qc/9712019.pdf Final Exam https://courses.physics.ucsd.edu/2015/Winter/physics225b/hw4-sols.pdf Week 7 Quantum Mechanics Lectures https://www.youtube.com/watch?v=ZcpwnozMh2U https://www.edx.org/course/quantum-mechanics-everyone-georgetownx-phyx-008-01x Study Guide https://ocw.mit.edu/courses/physics/8-04-quantum-physics-i-spring-2013/lecture-notes/MIT8_04S13_Lec01.pdf Final Exam http://www.physics.rutgers.edu/~haule/501/sol_final_2015.pdf Week 8 Quantum Field Theory Lectures https://www.youtube.com/watch?v=IGHvf9BwkDY&list=PLbMVogVj5nJQ3slQodXQ5cSEtcp4HbNFc Study Guide https://web.physics.ucsb.edu/~mark/ms-qft-DRAFT.pdf- Final Exam http://www-personal.umich.edu/~jbourj/peskin/Quantum%20Field%20Theory%20II%20homeworks.pdf Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams! Join us at the School of AI: https://theschool.ai/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w And please support me on Patreon: https://www.patreon.com/user?u=3191693

Getting Started with TensorFlow 2.0 (Google I/O'19)

Getting Started with TensorFlow 2.0 (Google I/O'19) [Collection] TensorFlow 2.0 is here! Understand new user-friendly APIs for beginners and experts through code examples to help you create different flavors of neural networks (Dense, Convolutional, and Recurrent) and understand when to use the Keras Sequential, Functional, and Subclassing APIs for your projects. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Josh Gordon, Paige Bailey TCDFE8

Machine Learning on Your Device: The Options (Google I/O'19)

Machine Learning on Your Device: The Options (Google I/O'19) [Collection] Developers have an often confusing plethora of options available to them in using machine learning to enhance their mobile apps and edge devices. This session will demystify these options, showing you how TensorFlow can be used to train models and how you can use these models across a variety of devices with TensorFlow Lite. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Laurence Moroney, Daniel Situnayake T6D370

5 Ways to Use Bitcoin

5 Ways to Use Bitcoin Example of a USD pegged cryptocurrency: https://nubits.com/ Create your own cryptocurrency using Colored Coins: https://www.coinprism.com/ Stellar: https://www.stellar.org/ GridCoin: http://www.gridcoin.us/ ZeroCoin: http://zerocoin.org/ LiteCoin: https://litecoin.org/ I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

What is Bitcoin?

What is Bitcoin? Comment! Like! Subscribe! I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Buy your first Bitcoin here: http://www.coinbase.com Bitcoin source code: https://github.com/bitcoin/bitcoin Cheap Bitcoin Miner: https://21.co/learn/ Expensive Bitcoin Miner: http://www.butterflylabs.com/ Good tutorials on building your first BTC apps: https://21.co/learn Great free online class for learning more about BTC: https://www.youtube.com/watch?v=fOMVZXLjKYo I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

Artificial Intelligence & the Future - Rise of AI (Elon Musk, Bill Gates, Sundar Pichai)|Simplilearn

Artificial Intelligence & the Future - Rise of AI (Elon Musk, Bill Gates, Sundar Pichai)|Simplilearn [Collection] Artificial Intelligence (AI) is currently the hottest buzzword in tech. Here is a video on the role of Artificial Intelligence and its scope in the future. We have put together the best clips on Artificial Intelligence by the most well-known leaders and influencers such as Bill Gates, Tim Cook, Warren Buffett, Barack Obama, Elon Musk, Sundar Pichai and Jeff Bezos. The last few years have seen a number of techniques that have previously been in the realm of science fiction slowly transform into reality. We have brought to you the business leaders of today speaking about artificial intelligence, what is fascinating about AI, the latest AI projects and what's in store for the future of AI. We will also answer the question of whether AI will someday overpower us humans. According to the report How AI Boosts Industry Profits and Innovations, AI is predicted to increase economic growth by an average of 1.7 percent across 16 industries by 2035. The report goes on to say that, by 2035, AI technologies could increase labor productivity by 40 percent or more, thereby doubling economic growth in 12 developed nations that continue to draw talented and experienced professionals to work in this domain. Let us see what our business leaders have to say about this. To learn more about Artificial Intelligence, subscribe to our YouTube channel: youtube.com/c/SimplilearnOfficial #ArtificialIntelligence #AI #MachineLearning #SimplilearnAI #SimplilearnTraining #DeepLearning #Simplilearn Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming. Why learn Artificial Intelligence? The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills. You can gain in-depth knowledge of Artificial Intelligence by taking our Artificial Intelligence certification training course. Those who complete the course will be able to: 1. Master the concepts of supervised and unsupervised learning 2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning. 6. Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems Learn more at: https://www.simplilearn.com/artificial-intelligence-masters-program-training-course?utm_campaign=Artificial-Intelligence-and-the-Future-wTbrk0suwbg&utm_medium=Tutorials&utm_source=youtube Video Credits: CNBC ( https://www.youtube.com/watch?v=HG2uDgQufho https://www.youtube.com/watch?v=nvMfFgIXV6w ) WIRED ( https://www.youtube.com/watch?v=72bHop6AIcc ) SXSW ( https://www.youtube.com/watch?v=kzlUyrccbos ) World Economic Forum ( https://www.youtube.com/watch?v=ApvbIIElwi8 ) TheBushCenter ( https://www.youtube.com/watch?v=V7TB7SHenk8 ) For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn/ - Website: https://www.simplilearn.com

Machine Learning In 5 Minutes | Machine Learning Introduction |What Is Machine Learning |Simplilearn

Machine Learning In 5 Minutes | Machine Learning Introduction |What Is Machine Learning |Simplilearn [Collection] This Machine Learning basics video will help you understand what is Machine Learning, what are the types of Machine Learning, what is Supervised & Unsupervised Machine Learning, what is Reinforcement Learning and will also explain how Machine Learning is being used in various businesses. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use “pattern recognition” to produce reliable results. Machine learning is starting to reshape how we live, and it’s time we understood what it is and why it matters. Now, let us deep dive into this short video on Machine learning and understand the basics of Machine Learning. Below topics are explained in this Machine Learning basics video: 1. What is Machine Learning? (00:52) 2. What is Supervised Learning? (01:25) 3. What is Unsupervised Learning? (01:52) 4. What is Reinforcement Learning? (02:25) 5. Simplilearn's Machine Learning certification course (03:54) Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy #MachineLearning #MachineLearningAlgorithms #WhatisMachineLearning #MachineLearningBasics #SimplilearnMachineLearning #MachineLearningCourse About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Machine-Learning-Introduction--DEL6SVRPw0&utm_medium=Tutorials&utm_source=youtube For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0

Deep Learning Interview Questions And Answers | AI & Deep Learning Interview Questions | Simplilearn

Deep Learning Interview Questions And Answers | AI & Deep Learning Interview Questions | Simplilearn [Collection] This Deep Learning interview questions and answers video will help you prepare for Deep Learning interviews. This video is ideal for both beginners as well as professionals who are appearing for Deep Learning, Machine Learning or Data Science interviews. Learn what are the most important Deep Learning interview questions and answers and know what will set you apart in the interview process. Some of the important Deep Learning interview questions are listed below: 1. What is Deep Learning? 2. What is a Neural Network? 3. What is a Multilayer Perceptron (MLP)? 4. What is Data Normalization and why do we need it? 5. What is a Boltzmann Machine? 6. What is the role of Activation Functions in neural network? 7. What is a cost function? 8. What is Gradient Descent? 9. What do you understand by Backpropagation? 10. What is the difference between Feedforward Neural Network and Recurrent Neural Network? 11. What are some applications of Recurrent Neural Network? 12. What are Softmax and ReLU functions? 13. What are hyperparameters? 14. What will happen if learning rate is set too low or too high? 15. What is Dropout and Batch Normalization? 16. What is the difference between Batch Gradient Descent and Stochastic Gradient Descent? 17. Explain Overfitting and Underfitting and how to combat them. 18. How are weights initialized in a network? 19. What are the different layers in CNN? 20. What is Pooling in CNN and how does it work? #DeepLearningInterviewQuestions #DeepLearning #MachineLearning #DataScience #SimplilearnDeepLearning Subscribe to our channel for more Deep Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/Yy74ga To gain in-depth knowledge of Deep Learning, check our Deep Learning Certification training course: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=Deep-Learning-interview-Questions-And-Answers-JYMKEM5c7PU&utm_medium=Tutorials&utm_source=youtube To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline 2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces We recommend this deep learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in deep learning Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=Deep-Learning-Interview-Questions-And-Answers-JYMKEM5c7PU&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0

Convolutional Neural Network Tutorial (CNN) | How CNN Works | Deep Learning Tutorial | Simplilearn

Convolutional Neural Network Tutorial (CNN) | How CNN Works | Deep Learning Tutorial | Simplilearn [Collection] This Convolutional neural network tutorial (CNN) will help you understand what is a convolutional neural network, how CNN recognizes images, what are layers in the convolutional neural network and at the end, you will see a use case implementation using CNN. CNN is a feed forward neural network that is generally used to analyze visual images by processing data with grid like topology. A CNN is also known as a "ConvNet". Convolutional networks can also perform optical character recognition to digitize text and make natural-language processing possible on analog and hand-written documents. CNNs can also be applied to sound when it is represented visually as a spectrogram. Now, lets deep dive into this video to understand what is CNN and how do they actually work. Below topics are explained in this CNN tutorial (Convolutional Neural Network Tutorial) 1. Introduction to CNN 2. What is a convolutional neural network? 3. How CNN recognizes images? 4. Layers in convolutional neural network 5. Use case implementation using CNN To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/ZNcp9n Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline 2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 4. Build deep learning models in TensorFlow and interpret the results 5. Understand the language and fundamental concepts of artificial neural networks 6. Troubleshoot and improve deep learning models 7. Build your own deep learning project 8. Differentiate between machine learning, deep learning and artificial intelligence There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in deep learning Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=Convolutional-Neural-Network-Tutorial-CNN-Tutorial-Jy9-aGMB_TE&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0

Recurrent Neural Network (RNN) Tutorial | RNN LSTM Tutorial | Deep Learning Tutorial | Simplilearn

Recurrent Neural Network (RNN) Tutorial | RNN LSTM Tutorial | Deep Learning Tutorial | Simplilearn [Collection] This Recurrent Neural Network tutorial will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this video and understand what is RNN and how does it actually work. Below topics are explained in this recurrent neural networks tutorial: 1. What is a neural network? 2. Popular neural networks? 3. Why recurrent neural network? 4. What is a recurrent neural network? 5. How does an RNN work? 6. Vanishing and exploding gradient problem 7. Long short term memory (LSTM) 8. Use case implementation of LSTM To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/wsjuLv Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline 2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 4. Build deep learning models in TensorFlow and interpret the results 5. Understand the language and fundamental concepts of artificial neural networks 6. Troubleshoot and improve deep learning models 7. Build your own deep learning project 8. Differentiate between machine learning, deep learning and artificial intelligence There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this Deep Learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in deep learning Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=Recurrent-Neural-Network-Tutorial-lWkFhVq9-nc&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0

TensorFlow Object Detection API Tutorial | Object Detection API | TensorFlow Tutorial | Simplilearn

TensorFlow Object Detection API Tutorial | Object Detection API | TensorFlow Tutorial | Simplilearn [Collection] This TensorFlow tutorial will take you through the TensorFlow code to perform object detection in a video. TensorFlow object detection API which is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models and also it provides a collection of detection models pre-trained on the COCO dataset, the Kitti dataset, and the Open Images dataset. One among the many detection models is the combination of single shot detectors (SSDs) and MobileNets architecture which is fast, efficient and does not require the huge computational capability to accomplish the object detection task, an example of which can be seen in this tutorial. Now, let's get started and understand how object detection using TensorFlow works. To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Watch more videos on Deep Learning: https://www.youtube.com/playlist?list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline 2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 4. Build deep learning models in TensorFlow and interpret the results 5. Understand the language and fundamental concepts of artificial neural networks 6. Troubleshoot and improve deep learning models 7. Build your own deep learning project 8. Differentiate between machine learning, deep learning and artificial intelligence There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in deep learning Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=TensorFlow-Object-Detection-hs-9BUAPN3Y&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0

TensorFlow Tutorial | Deep Learning with TensorFlow | TensorFlow Tutorial for Beginners |Simplilearn

TensorFlow Tutorial | Deep Learning with TensorFlow | TensorFlow Tutorial for Beginners |Simplilearn [Collection] This TensorFlow tutorial will help you understand what is Deep Learning and it's libraries, why use TensorFlow, what is TensorFlow, how to build a computational graph, programming using elements in TensorFlow, what are Recurrent Neural Networks along with a use case implementation on TensorFlow. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this video, you will learn the fundamentals of TensorFlow concepts, functions and operations required to implement deep learning algorithms and leverage data like never before. Now let's get started in mastering the concept of Deep Learning using TensorFlow. Below topics are explained in this TensorFlow Tutorial: 1. What is Deep Learning? 2. Top Deep Learning libraries? 3. Why use TensorFlow? 4. What is TensorFlow? 5. Building a computational graph 6. Programming elements in TensorFlow 7. Introducing Recurrent Neural Networks 8. Use case implementation of RNN using TensorFlow To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/TY2D7H Watch more videos on Deep Learning: https://www.youtube.com/playlist?list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline 2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 4. Build deep learning models in TensorFlow and interpret the results 5. Understand the language and fundamental concepts of artificial neural networks 6. Troubleshoot and improve deep learning models 7. Build your own deep learning project 8. Differentiate between machine learning, deep learning and artificial intelligence There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in deep learning Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=What-is-Deep-Learning-_NMI8peAmNA&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0

Installing TensorFlow on Ubuntu | How to Install TensorFlow on Ubuntu | Simplilearn

Installing TensorFlow on Ubuntu | How to Install TensorFlow on Ubuntu | Simplilearn [Collection] This TensorFlow installation video will guide you on how to install TensorFlow on Ubuntu 14.04 LTS using Python 3.4 version and TensorFlow 1.5. This TensorFlow installation tutorial is ideal for beginners to learn how to install TensorFlow smoothly on their Ubuntu systems as well as Windows by creating a VirtualBox. To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Watch more videos on Deep Learning: https://www.youtube.com/playlist?list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline 2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 4. Build deep learning models in TensorFlow and interpret the results 5. Understand the language and fundamental concepts of artificial neural networks 6. Troubleshoot and improve deep learning models 7. Build your own deep learning project 8. Differentiate between machine learning, deep learning and artificial intelligence There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in deep learning Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=Installing-Tensorflow-On-Ubuntu-Ejzubp-B83o&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0

What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial for Beginners | Simplilearn

What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial for Beginners | Simplilearn [Collection] This TensorFlow tutorial will help you in understanding what exactly is TensorFlow and how it is used in Deep Learning. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this tutorial, you will learn the fundamentals of TensorFlow concepts, functions, and operations required to implement deep learning algorithms and leverage data like never before. This TensorFlow tutorial is ideal for beginners who want to pursue a career in Deep Learning. Now, let us deep dive into this TensorFlow tutorial and understand what TensorFlow actually is and how to use it. Below topics are explained in this TensorFlow Tutorial: 1. What is Deep Learning? 2. Top Deep Learning Libraries 3. Why TensorFlow? 4. What is TensorFlow? 5. What are Tensors? 6. What is a Data Flow Graph? 7. Program Elements in TensorFlow 8. Use case implementation using TensorFlow To learn more about TensorFlow and Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/5krhus Watch more videos on Deep Learning: https://www.youtube.com/playlist?list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip TensorFlow installation Tutorial: https://www.youtube.com/watch?v=Ejzubp-B83o&t=710s #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse #TensorFlow Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline 2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 4. Build deep learning models in TensorFlow and interpret the results 5. Understand the language and fundamental concepts of artificial neural networks 6. Troubleshoot and improve deep learning models 7. Build your own deep learning project 8. Differentiate between machine learning, deep learning and artificial intelligence There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in deep learning Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=What-is-TensorFlow-E8n_k6HNAgs&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0

Neural Network Tutorial | Artificial Neural Network Tutorial | Deep Learning Tutorial | Simplilearn

Neural Network Tutorial | Artificial Neural Network Tutorial | Deep Learning Tutorial | Simplilearn [Collection] This Neural Network tutorial will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a usecase implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into this video to understand how a neural network actually work. Below topics are explained in this neural network Tutorial: 1. What is Neural Network? 2. What can Neural Network do? 3. How does Neural Network work? 4. Types of Neural Network 5. Use case - To classify between the photos of dogs and cats To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/Gn1frA Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline 2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 4. Build deep learning models in TensorFlow and interpret the results 5. Understand the language and fundamental concepts of artificial neural networks 6. Troubleshoot and improve deep learning models 7. Build your own deep learning project 8. Differentiate between machine learning, deep learning and artificial intelligence There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in deep learning Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=Neural-Network-Tutorial-ysVOhBGykxs&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn/ - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0

What is a Neural Network? | How Deep Neural Networks Work | Neural Network Tutorial | Simplilearn

What is a Neural Network? | How Deep Neural Networks Work | Neural Network Tutorial | Simplilearn [Collection] This Neural Network tutorial will help you understand what is deep learning, what is a neural network, how deep neural network works, advantages of neural network, applications of neural network and the future of neural network. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Deep Learning forms the basis for most of the incredible advances in Machine Learning. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. Now, let us deep dive into this video to understand how a neural network actually works along with some real-life examples. Below topics are explained in this neural network Tutorial: 1. What is Deep Learning? 2. What is an artificial network? 3. How does neural network work? 4. Advantages of neural network 5. Applications of neural network 6. Future of neural network To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/Hk7cJ1 Watch more videos on Deep Learning: https://www.youtube.com/playlist?list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline 2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 4. Build deep learning models in TensorFlow and interpret the results 5. Understand the language and fundamental concepts of artificial neural networks 6. Troubleshoot and improve deep learning models 7. Build your own deep learning project 8. Differentiate between machine learning, deep learning and artificial intelligence There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in deep learning Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=What-is-a-nEURAL-nETWORK-VB1ZLvgHlYs&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0

Deep Learning Tutorial - Part 2 | TensorFlow Object Detection | TensorFlow Tutorial | Simplilearn

Deep Learning Tutorial - Part 2 | TensorFlow Object Detection | TensorFlow Tutorial | Simplilearn [Collection] This Deep Learning tutorial will take you through the TensorFlow code to perform object detection in an image. You will understand what is object detection API, what are the libraries required for object detection, what is a COCO dataset and at the end, we will implement a code to detect objects in few images. TensorFlow object detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models and also it provides a collection of detection models pre-trained on the COCO dataset, the Kitti dataset, and the Open Images dataset. One among the many detection models is the combination of single shot detectors (SSDs) and MobileNets architecture. An example of this model is shown in this Deep Learning Tutorial. Now, lets get started and understand how object detection using TensorFlow works. Below topics are covered in this Deep Learning tutorial: 1. TensorFlow object detection API 2. Libraries required 3. COCO dataset 4. Implementation of object detection API To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Watch more videos on Deep Learning: #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline 2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 4. Build deep learning models in TensorFlow and interpret the results 5. Understand the language and fundamental concepts of artificial neural networks 6. Troubleshoot and improve deep learning models 7. Build your own deep learning project 8. Differentiate between machine learning, deep learning and artificial intelligence There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in deep learning Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=DeepLearning-Tutorial-6EALSPZZagg&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn/ - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0