Free artificial intelligence and machine learning video tutorial resource: 2019

Wednesday, August 7, 2019

Answering your TF Lite questions and more! (#AskTensorFlow)

Answering your TF Lite questions and more! (#AskTensorFlow) [Collection] Developer Advocate Paige Bailey (@DynamicWebPaige) and TF Developer Advocate Daniel Situnayake answer your #AskTensorFlow questions. Remember to use #AskTensorFlow to have your questions answered in a future episode! 0:21 - Is RNN / LSTM, quantization-aware training, and TOCO conversion in TF Lite available in TensorFlow 2.0? 1:22 - Is there any tutorial / example for text processing models in TF Lite, aside from the pre-trained smart reply example? 1:53 - Is Swift for TensorFlow for iOS programming? 2:37 - Will there be a commodity device that I can use for TPU inferencing? 3:43 - Does TF Lite only work on Coral dev boards? 4:41 - Will Edge TPUs be available to purchase in other countries? 5:19 - What about Android things? Does TF 2.0 support them? 6:08 - What platforms are supported by Swift for TensorFlow? 6:55 - Will there be support in the Python API for exporting object detection models (e.g., after transfer learning) to TF Lite? 8:28 - Why is it currently so difficult to integrate and use custom C++ / CUDA operations in TensorFlow and especially TensorFlow Serving? Are there any plans to make this process easier for production? 9:28 - I had some problems using Keras and TensorFlow + OpenCV. Are there any improvements in TensorFlow 2.0? 10:44 - Does TensorFlow have any API that can do AutoML, as Azure ML SDK? 11:40 - What about Kotlin for TensorFlow? 12:12 - Can a deep learning model be miniaturized automatically? 13:19 - Regarding tf.data, do you guys have any new APIs to directly load audio files (.wav, etc.) instead of going through the extra conversion steps to convert to TFRecords? 14:14 - Do you have any plans to add support for constraints or -even better- AutoDiff on manifolds? It would be so nice to do optimization where some parameters live in SO(3), for example. Resources mentioned in this episode: TF Lite mailing list - http://bit.ly/2KbyHBl Swift for TensorFlow mailing list - http://bit.ly/338ydDw Guide on RNNs & LSTMs in TF Lite - http://bit.ly/2KgvyPn TOCO converter guide - http://bit.ly/2MzvPQ4 Post-training quantization - http://bit.ly/2KnWE74 Coral platform - http://bit.ly/2ZlMbiS TF Lite models - http://bit.ly/32XGGZU MLIR: A new intermediate representation and compiler framework - http://bit.ly/2KnX25w Cloud AutoML - http://bit.ly/2Yghy1Q TensorFlow model optimization toolkit - http://bit.ly/2K9R2OS tf.io - http://bit.ly/2YzmqdB 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

Friday, July 12, 2019

TensorFlow.js (TensorFlow Meets)

TensorFlow.js (TensorFlow Meets) [Collection] On this episode of TensorFlow Meets, Laurence (@lmoroney) talks with Yannick Assogba, Front End Software Engineer on the TensorFlow team. Learn about how to get started with TensorFlow.js as the 1.0 version is now available. Comment below for any questions! This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish. TensorFlow.js → http://bit.ly/2L6Bq0k TensorFlow.js 1.0 (TF Dev Summit ‘19) → http://bit.ly/2S74ohl BodyPix - person segmentation in the browser → http://bit.ly/30iFI8z Toxicity classifier → http://bit.ly/2NQfkBk Universal sentence encoder → http://bit.ly/2LKiHHq Subscribe to the TensorFlow channel → https://goo.gle/2WtM7Ak Watch more episodes of TensorFlow Meets → https://goo.gle/2Z8zCXJ

Wednesday, July 3, 2019

TensorFlow 2.0 and Keras (#AskTensorFlow)

TensorFlow 2.0 and Keras (#AskTensorFlow) [Collection] Developer Advocate Paige Bailey (@DynamicWebPaige) and TF Software Engineer Alex Passos answer your #AskTensorFlow questions. Remember to use #AskTensorFlow to have your questions answered in a future episode! 0:18 - What will be the support model for stand-alone Keras? 1:01 - Does tf.keras include everything that stand-alone Keras includes? 1:44 - What will TensorFlow 2.0 change to stand-alone Keras? 2:24 - Is there support for Bayesian layers in tf.keras? 2:54 - Can I create custom layers through tf.keras? 3:37 - Will the Keras namespace be removed in future releases of TF 2.0? 4:15 - Can we use SavedModel for a Keras model? Keras Special Interest Group: http://bit.ly/2Xc6Sko Join the TF community: http://bit.ly/2KFIXDo TF Probability port for Bayesian Methods for Hackers: http://bit.ly/2RBwa5d 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

Friday, June 28, 2019

Inside TensorFlow: Functions, not sessions

Inside TensorFlow: Functions, not sessions [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! In this training session with TensorFlow Software Engineer Alexandre Passos, we go over the history of tf.Session, the drawbacks with it, and the motivation for its replacement, tf.function. We also go over the main classes and functions in the front-end which are used to generate graphs from tf.functions. Let us know what you think about this presentation in the comments below! TensorFlow on GitHub → https://goo.gle/2KYPXdS Watch more from Inside TensorFlow Playlist → https://bit.ly/2JBXFtt Subscribe to the TensorFlow channel → https://bit.ly/TensorFlow1

Saturday, June 15, 2019

TensorFlow 2.0: An Overview (#AskTensorFlow)

TensorFlow 2.0: An Overview (#AskTensorFlow) [Collection] Developer Advocate Paige Bailey (@DynamicWebPaige) and TF Software Engineer Alex Passos answer your #AskTensorFlow questions. Learn about the latest improvements to TensorFlow Core in TF 2.0, how to create custom layers with tf.keras, differences between Keras and tf.keras, and compatibility with Keras and Python 3.7. Remember to use #AskTensorFlow to have your questions answered in a future episode! Get started with TensorFlow 2.0 → http://bit.ly/2IeWT3e Join the TensorFlow community → http://bit.ly/2Wqv81N tf.estimator.BoostedTreesRegressor → http://bit.ly/2WqP0lk tf.estimator.BoostedTreesClassifier → http://bit.ly/2Mywz9I tf.contrib → http://bit.ly/2XzSGOi Binaries for Python 3.7 → http://bit.ly/2My40cH Support model for Keras → http://bit.ly/2ZbXKc0 tf.keras → http://bit.ly/31dVeDU TensorFlow probability → http://bit.ly/2XvPymB TensorFlow probability presentation → http://bit.ly/2ImVXtS Custom layers in tf.keras → http://bit.ly/2QVbg0w SavedModel → http://bit.ly/2R3fzHz This video is also subtitled in Chinese, Indonesian, Italian, French, German, Japanese, Korean, Portuguese, and Spanish. Subscribe to the TensorFlow channel → http://bit.ly/TensorFlow1 Watch more episodes of #AskTensorFlow → http://bit.ly/2JcL3tT

Saturday, June 8, 2019

Working with TensorFlow Datasets (TensorFlow Meets)

Working with TensorFlow Datasets (TensorFlow Meets) [Collection] On this episode of TensorFlow Meets, Laurence Moroney (@lmoroney) talks with Ryan Sepassi, Google AI Research Software Engineer, about TensorFlow datasets, how it can be used to standardize the interface to a lot of public research datasets, and how tfds preprocesses the formats of source datasets into a standard format that is ready to be fed into the machine learning pipeline. TensorFlow Datasets on GitHub → http://bit.ly/2JRrRBY TensorFlow Datasets → http://bit.ly/2HR0Jkt 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

Thursday, June 6, 2019

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

Inside TensorFlow: Resources and Variants

Inside TensorFlow: Resources and Variants [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 resources and variants with Alexandre Passos, a Software Engineer on the TensorFlow team. This training session goes over how state is managed in TensorFlow, and how dynamic C++ types are supported in graphs. We explore the stateful bit, ref edges, dt_resource tensors, resource variables, tensor lists, and variant types. Let us know what you think about this presentation in the comments below! 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

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

Wednesday, June 5, 2019

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

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

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

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

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

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

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

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

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

Using GPUs in TensorFlow, TensorBoard in notebooks, finding new datasets, & more! (#AskTensorFlow)

Using GPUs in TensorFlow, TensorBoard in notebooks, finding new datasets, & more! (#AskTensorFlow) [Collection] In a special live episode from the TensorFlow Dev Summit, Paige (@DynamicWebPaige) and Laurence (@lmoroney) answer your #AskTensorFlow questions. Learn about using GPU in TensorFlow, saving models as a SavedModel, running TensorBoard on Colab, using feature columns with Keras, and where to find new datasets. Remember to use #AskTensorFlow to have your questions answered in a future episode! TensorBoard in notebooks → https://bit.ly/2DLNgIf TF high-level APIs → https://bit.ly/2WdjXFG Introducing TensorFlow Datasets → https://bit.ly/2PvTkZW CheXpert Dataset → https://bit.ly/2WbeEXg This video is also subtitled in Chinese, Indonesian, Italian, French, German, Japanese, Korean, Portuguese, and Spanish. Subscribe to the TensorFlow channel → http://bit.ly/TensorFlow1 Watch more episodes of #AskTensorFlow → http://bit.ly/2JcL3tT

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

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

Inside TensorFlow: tf.data - TF Input Pipeline

Inside TensorFlow: tf.data - TF Input Pipeline [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 tf.data, which is TensorFlow’s input pipeline. Basic familiarity with TensorFlow concepts is useful to integrate these tips. You will learn about Python view, C++ view, support for non-tensor types, static optimizations, and dynamic optimizations. Let us know what you think about this presentation in the comments below! Our camera briefly stopped working about halfway through, so apologies for the short section of reduced audio quality. Importing Data with TensorFlow → http://bit.ly/2WeZ4tv Subscribe to the TensorFlow channel → http://bit.ly/TensorFlow1

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

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

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

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

Using callbacks in training, getting started in TF 2.0, & more! (#AskTensorFlow)

Using callbacks in training, getting started in TF 2.0, & more! (#AskTensorFlow) [Collection] In this special live episode from TensorFlow Dev Summit ‘19, Paige (@DynamicWebPaige) and Laurence (@lmoroney) answer your #AskTensorFlow questions! Learn about using callbacks to cancel training once you’ve reached your desired accuracy, how to get started with TensorFlow 2.0 if you’re new to machine learning, and we’ll show you a fun example of image classification in the browser. Remember to use #AskTensorFlow to have your questions answered in a future episode! TensorFlow.js demos → https://bit.ly/2KW2wb8 Cloud Functions for Firebase → https://bit.ly/2Xn2E4Z TensorFlow for poets codelab → https://bit.ly/2Hk9zDv TensorFlow Lite examples → https://bit.ly/2XoYSYV This video is also subtitled in Chinese, Indonesian, Italian, French, German, Japanese, Korean, Portuguese, and Spanish. Subscribe to the TensorFlow channel → http://bit.ly/TensorFlow1 Watch more episodes of #AskTensorFlow → http://bit.ly/2JcL3tT

1.What are we going to build ?

1.What are we going to build ? [Collection] Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud Welcome to "The AI University". Subtitles available in: Hindi, English, French ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App About this Channel: The AI University is a channel which is on a mission to democratize the Artificial Intelligence, Big Data Hadoop and Cloud Computing education to the entire world. The aim of this channel is to impart the knowledge to the data science, data analysis, data engineering and cloud architecture aspirants as well as providing advanced knowledge to the ones who already possess some of this knowledge. Please share, comment, like and subscribe if you liked this video. If you have any specific questions then you can comment on the comment section and I'll definitely try to get back to you. #DataScience #AI #TheAIUniversity

5.Deploy ML on Cloud - Multiple Linear Regression Code Part 1

5.Deploy ML on Cloud - Multiple Linear Regression Code Part 1 [Collection] Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Deploy ML on Cloud - Multiple Linear Regression Code Part 1 Welcome to "The AI University". Subtitles available in: Hindi, English, French ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App About this Channel: The AI University is a channel which is on a mission to democratize the Artificial Intelligence, Big Data Hadoop and Cloud Computing education to the entire world. The aim of this channel is to impart the knowledge to the data science, data analysis, data engineering and cloud architecture aspirants as well as providing advanced knowledge to the ones who already possess some of this knowledge. Please share, comment, like and subscribe if you liked this video. If you have any specific questions then you can comment on the comment section and I'll definitely try to get back to you. #DataScience #AI #TheAIUniversity

3.Deploy ML on Cloud - Problem Statement

3.Deploy ML on Cloud - Problem Statement [Collection] Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Deploy ML on Cloud - Problem Statement Welcome to "The AI University". Subtitles available in: Hindi, English, French ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App About this Channel: The AI University is a channel which is on a mission to democratize the Artificial Intelligence, Big Data Hadoop and Cloud Computing education to the entire world. The aim of this channel is to impart the knowledge to the data science, data analysis, data engineering and cloud architecture aspirants as well as providing advanced knowledge to the ones who already possess some of this knowledge. Please share, comment, like and subscribe if you liked this video. If you have any specific questions then you can comment on the comment section and I'll definitely try to get back to you. #DataScience #AI #TheAIUniversity

2.Deploy ML on Cloud - Demo

2.Deploy ML on Cloud - Demo [Collection] Welcome to "The AI University". Subtitles available in: Hindi, English, French Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud. Deploy ML on Cloud - Demo ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App About this Channel: The AI University is a channel which is on a mission to democratize the Artificial Intelligence, Big Data Hadoop and Cloud Computing education to the entire world. The aim of this channel is to impart the knowledge to the data science, data analysis, data engineering and cloud architecture aspirants as well as providing advanced knowledge to the ones who already possess some of this knowledge. Please share, comment, like and subscribe if you liked this video. If you have any specific questions then you can comment on the comment section and I'll definitely try to get back to you. #DataScience #AI #TheAIUniversity

4.Deploy ML on Cloud - Simple and Multiple Linear Regression Explanation

4.Deploy ML on Cloud - Simple and Multiple Linear Regression Explanation [Collection] Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Deploy ML on Cloud - Simple and Multiple Linear Regression Explanation Welcome to "The AI University". Subtitles available in: Hindi, English, French ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App About this Channel: The AI University is a channel which is on a mission to democratize the Artificial Intelligence, Big Data Hadoop and Cloud Computing education to the entire world. The aim of this channel is to impart the knowledge to the data science, data analysis, data engineering and cloud architecture aspirants as well as providing advanced knowledge to the ones who already possess some of this knowledge. Please share, comment, like and subscribe if you liked this video. If you have any specific questions then you can comment on the comment section and I'll definitely try to get back to you. #DataScience #AI #TheAIUniversity

Tuesday, May 28, 2019

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

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

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

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

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

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

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

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

ML & AI sandbox demos at Google I/O 2019

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

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

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

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

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

TensorFlow Lite for on-device ML (TensorFlow Meets)

Inside TensorFlow: Summaries and TensorBoard

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

Swift for TensorFlow (Google I/O'19)

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

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

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

Monday, May 27, 2019

ML & AI sandbox demos at Google I/O 2019

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

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

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)

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

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!

Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners Part - 1 | Simplilearn

Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners Part - 1 | Simplilearn [Collection] This Machine Learning tutorial will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. 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, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning tutorial video and understand what it is and why it matters. Machine Learning Tutorial Part - 2: https://www.youtube.com/watch?v=_Wkx_447zBM Below topics are explained in this Machine Learning tutorial: 1. Why Machine Learning? ( 00:45 ) 2. What is Machine Learning? ( 04:52 ) 3. Types of Machine Learning ( 11:34 ) 4. Machine Learning Algorithms ( 16:41 ) - Linear Regression ( 16:57 ) - Decision Trees ( 25:43 ) - Support Vector Machine ( 34:00 ) 5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM ( 36:02 ) Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/m5Txob Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy #MachineLearning #MachineLearningAlgorithms #Datasciencecourse #DataScience #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-Tutorial-DWsJc1xnOZo&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

Machine Learning Interview Questions And Answers | Data Science Interview Questions | Simplilearn

Machine Learning Interview Questions And Answers | Data Science Interview Questions | Simplilearn [Collection] This Machine Learning Interview Questions And Answers video will help you prepare for Data Science and Machine learning interviews. This video is ideal for both beginners as well as professionals who are appearing for Machine Learning or Data Science interviews. Learn what are the most important Machine Learning interview questions and answers and know what will set you apart in the interview process. Some of the important Machine Learning Interview Questions are listed below: 1. What are the different types of Machine Learning? 2. What is overfitting? And how can you avoid it? 3. What is false positive and false negative and how are they significant? 4. What are the three stages to build a model in Machine Learning? 5. What is Deep Learning? 6. What are the differences between Machine Learning and Deep Learning? 7. What are the applications of supervised Machine Learning in modern businesses? 8. What is semi-supervised Machine Learning? 9. What are the unsupervised Machine Learning techniques? 10. What is the difference between supervised and unsupervised Machine Learning? 11. What is the difference between inductive Machine Learning and deductive Machine Learning? 12. What is 'naive' in the Naive Bayes classifier? 13. What are Support Vector Machines? 14. How is Amazon able to recommend other things to buy? How does it work? 15. When will you use classification over regression? 16. How will you design an email spam filter? 17. What is Random Forest? 18. What is bias and variance in a Machine Learning model? 19. What’s the trade-off between bias and variance? 20. What is pruning in decision trees and how is it done? Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Machine-Learning-interview-Questions-and-answers-hB1CTizqGFk&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Machine-Learning-interview-Questions-and-answers-hB1CTizqGFk&utm_medium=Tutorials&utm_source=youtube You can also go through the Slides here: https://goo.gl/rmzjaQ #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - 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 - - - - - - - Who should take this Machine Learning Training Course? 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 - - - - - - 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

Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn

Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn [Collection] This Linear Regression in Machine Learning video will help you understand the basics of Linear Regression algorithm - what is Linear Regression, why is it needed and how Simple Linear Regression works with solved examples, Linear regression analysis, applications of Linear Regression and Multiple Linear Regression model. At the end, we will implement a use case on profit estimation of companies using Linear Regression in Python. This Machine Learning tutorial is ideal for beginners who want to understand Data Science algorithms as well as Machine Learning algorithms. Below topics are covered in this Linear Regression Machine Learning Tutorial: 1. Introduction to Machine Learning 2. Machine Learning Algorithms 3. Applications of Linear Regression 4. Understanding Linear Regression 5. Multiple Linear Regression 6. Usecase - Profit estimation of companies What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Linear-Regression-NUXdtN1W1FE&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Linear-Regression-NUXdtN1W1FE&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #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 - - - - - - - Who should take this Machine Learning Training Course? 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 - - - - - - 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

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