TensorFlow Extended (TFX) and Metadata (TensorFlow Meets)

TensorFlow Extended (TFX) and Metadata (TensorFlow Meets) [Collection] On this episode of TensorFlow Meets, Laurence (@lmoroney) talks with Clemens Mewald (@ClemensMewald) about TensorFlow Extended (TFX) specially built for product teams to accelerate the path from training model to deployment and production. Learn about the latest releases, metadata tracking, and more. This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish. TensorFlow Extended (TFX) Guide → https://goo.gle/2MoPZ0I TFX Tutorials → https://goo.gle/2WcQVdf Subscribe to the TensorFlow channel → https://bit.ly/TensorFlow1 Watch more episodes of TensorFlow Meets → https://bit.ly/2lbyLDK

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

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

Arun Subramaniyan discusses probabilistic modeling (TensorFlow Meets)

Arun Subramaniyan discusses probabilistic modeling (TensorFlow Meets) [Collection] In this episode of TensorFlow Meets, Laurence Moroney sits down with Arun Subramaniyan, VP Data Science & Analytics at BHGE Digital to discuss probabilistic modeling. TensorFlow Meets Playlist → http://bit.ly/2XFlRiM Please remember to subscribe to the TensorFlow Channel → http://bit.ly/2Rd2nQo

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

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

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

High-Level APIs in TensorFlow 2.0 (TensorFlow Meets)

High-Level APIs in TensorFlow 2.0 (TensorFlow Meets) [Collection] TensorFlow’s high-level APIs help are there to help you through each stage of your model-building process. On this episode of TensorFlow Meets, Laurence (@lmoroney) talks with TensorFlow Engineering Manager Karmel Allison about how TF 2.0 will make building models much easier. They also discuss distribution strategies and how TensorFlow brings the performance and deployability of graph style code to eager execution. Find out more about TensorFlow 2.0 → http://bit.ly/2UHFxR7 What’s coming in TensorFlow 2.0 → http://bit.ly/2IC5G2J Subscribe to the TensorFlow channel → http://bit.ly/TensorFlow1 Watch more episodes of TensorFlow Meets → http://bit.ly/2lbyLDK

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

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

How TensorFlow keeps improving (TensorFlow Meets)

How TensorFlow keeps improving (TensorFlow Meets) [Collection] On this episode of TensorFlow Meets, Laurence (@lmoroney) talks with TensorFlow Engineering Director Megan Kacholia about how TensorFlow has evolved to make it even easier for developers to get started with machine learning for a variety of applications, and how to stay updated on the TF 2.0 release. 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

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

Swift for TensorFlow (TensorFlow Meets)

Swift for TensorFlow (TensorFlow Meets) [Collection] It’s still the early days for Swift for TensorFlow, but Jeremy Howard is embracing the language for use in high performance numeric computing. On this episode of TensorFlow Meets, Josh (@random_forests) talks to the renowned data scientist and fast.ai founder about the future of Swift for TensorFlow, as well as the Swift online course coming to fast.ai in June 2019. fast.ai embracing Swift for Deep Learning → http://bit.ly/2GlFK7c Practical Deep Learning for Coders → https://bit.ly/2KQVfJW Subscribe to the TensorFlow channel → http://bit.ly/TensorFlow1 Watch more episodes of TensorFlow Meets → http://bit.ly/2lbyLDK

Inside TensorFlow: Control Flow

Inside TensorFlow: Control Flow [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, TF Software Engineer Skye Wanderman-Milne goes over Control Flow in TensorFlow, from its low-level ops and base APIs, through new “functional” ops in Control Flow v2. Let us know what you think in the comments below! Subscribe to the TensorFlow channel → http://bit.ly/TensorFlow1

4.One Hot Encoding to process Categorical variables (Python)

4.One Hot Encoding to process Categorical variables (Python) [Collection] One Hot Encoding to process Categorical variables (Python) This video explains categorical variables and how to encode it i.e. converting variables with values in string or text form to numeric value as well how to do one hot encoding. ********Git Hub Link for DataSet and Python Code********* https://github.com/nitinkaushik01/Machine_Learning_Data_Preprocessing_Python

3.Dataset Missing Values & Imputation (Detailed Python Tutorial)

3.Dataset Missing Values & Imputation (Detailed Python Tutorial) [Collection] Dataset Missing Values & Imputation (Detailed Python Tutorial) This video explains how to preprocess data, what are some of the reasons we get this missing data, how to identify the missing values and the various ways using which we can handle missing values. This is a very important step before we build machine learning models. ********Git Hub Link for DataSet and Python Code********* https://github.com/nitinkaushik01/Machine_Learning_Data_Preprocessing_Python

2.Introduction to Python Datasets (.csv files)

2.Introduction to Python Datasets (.csv files) [Collection] Introduction to Python Datasets (.csv files) This video introduces dataset i.e. what exactly is dataset, how do we import dataset in python which is in csv or comma separated value format so that you could use that data for building machine learning models. ********Git Hub Link for DataSet and Python Code********* https://github.com/nitinkaushik01/Machine_Learning_Data_Preprocessing_Python

1.Introduction to Python libraries(Data Scientist's arsenal)

1.Introduction to Python libraries(Data Scientist's arsenal) [Collection] Introduction to Python libraries(Data Scientist's arsenal) This video explains Python Libraries i.e. what exactly is a library? why do we use it? as well as what are some of the libraries in python which are used by almost all the data scientists. ********Git Hub Link for DataSet and Python Code********* https://github.com/nitinkaushik01/Machine_Learning_Data_Preprocessing_Python

4.Vnet to Vnet Peering Azure Virtual Network(Certification Topic)

4.Vnet to Vnet Peering Azure Virtual Network(Certification Topic) [Collection] Vnet to Vnet Peering Azure Virtual Network(Certification Topic)

3.Route Table Explained Azure Virtual Network (Certification Topic)

3.Route Table Explained Azure Virtual Network (Certification Topic) [Collection] Route Table Explained Azure Virtual Network (Certification Topic)

2.Create Public IP Azure Virtual Network (Certification Topic)

2.Create Public IP Azure Virtual Network (Certification Topic) [Collection] Create Public IP Azure Virtual Network (Certification Topic) Tutorials Series to learn about Microsoft Azure Virtual Network, Public IP, Private IP, Route Table, Vnet to Vnet Peering - Nitin Kaushik

1. Azure Virtual Networks Explained (Certification Topic)

1. Azure Virtual Networks Explained (Certification Topic) [Collection] Azure Virtual Networks Explained (Certification Topic) Tutorials Series to learn about Microsoft Azure Virtual Network, Public IP, Private IP, Route Table, Vnet to Vnet Peering - Nitin Kaushik

4. Azure Kubernetes Services - Execute the Code (GIT Clone)

4. Azure Kubernetes Services - Execute the Code (GIT Clone) [Collection] Azure Kubernetes Services - Execute the Code (GIT Clone)

3.Azure Kubernetes - Run Docker Container

3.Azure Kubernetes - Run Docker Container [Collection] Azure Kubernetes Run Docker Container

2. Azure Kubernetes - Spin up Cluster and Run the App

2. Azure Kubernetes - Spin up Cluster and Run the App [Collection] Azure Kubernetes Spin up Cluster and Run the App

1.Microsoft Azure Kubernetes Service - Introduction

1.Microsoft Azure Kubernetes Service - Introduction [Collection] Microsoft Azure Kubernetes Service - Introduction

4. Microsoft Azure Storage Replication & Redundancy

4. Microsoft Azure Storage Replication & Redundancy [Collection] Microsoft Azure Storage Replication & Redundancy Tutorials Series to learn about Microsoft Azure Storage, Access Keys, Secure Access Signature, Storage Explorer and Replication - Nitin Kaushik

3.Microsoft Azure Storage Explorer Explained

3.Microsoft Azure Storage Explorer Explained [Collection] Microsoft Azure Storage Explorer Explained Tutorials Series to learn about Microsoft Azure Storage, Access Keys, Secure Access Signature, Storage Explorer and Replication - Nitin Kaushik

2.Microsoft Azure Storage Access Keys and Secure Access Signature

2.Microsoft Azure Storage Access Keys and Secure Access Signature [Collection] Microsoft Azure Storage Access Keys and Secure Access Signature Tutorials Series to learn about Microsoft Azure Storage, Access Keys, Secure Access Signature, Storage Explorer and Replication - Nitin Kaushik

1. Microsoft Azure Storage Quick Introduction

1. Microsoft Azure Storage Quick Introduction [Collection] Microsoft Azure Storage Quick Introduction Tutorials Series to learn about Microsoft Azure Storage, Access Keys, Secure Access Signature, Storage Explorer and Replication - Nitin Kaushik

15. SQOOP command to export flat file from HDFS to Azure SQL Database

15. SQOOP command to export flat file from HDFS to Azure SQL Database [Collection] SQOOP command to export flat file from HDFS to Azure SQL Database Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik

14.Create Hive Job to Insert data from csv file to Hive Table | Azure

14.Create Hive Job to Insert data from csv file to Hive Table | Azure [Collection] Create Hive Job to Insert data from csv file to Hive Table | Azure Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik

13. Upload csv file from local system to Hadoop | Azure HDInsight

13. Upload csv file from local system to Hadoop | Azure HDInsight [Collection] Upload csv file from local system to Hadoop | Azure HDInsight Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik

12.Create Azure SQL Database and Table

12.Create Azure SQL Database and Table [Collection] Create Azure SQL Database and Table Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik URL for downloading SQL Server Management Studio : https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-2017

11.Delete Resources on Azure to save cost

11.Delete Resources on Azure to save cost [Collection] Delete Resources on Azure to save cost Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik

10.Deploy Spark Cluster and enable Jupyter Notebook

10.Deploy Spark Cluster and enable Jupyter Notebook [Collection] Deploy Spark Cluster and enable Jupyter Notebook Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik

3. Azure Virtual Machine Primer

3. Azure Virtual Machine Primer [Collection] Azure Virtual Machine Primer | Azure Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik

9.Create Hadoop Cluster and run Hive query

9.Create Hadoop Cluster and run Hive query [Collection] Create Hadoop Cluster and run Hive query | Azure Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik

8.Virtual Machine Scale Set Primer

8.Virtual Machine Scale Set Primer [Collection] Virtual Machine Scale Set Primer | Azure Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik

7.Performance Monitoring of Virtual Machines

7.Performance Monitoring of Virtual Machines [Collection] Performance Monitoring of Virtual Machines | Azure Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik

6.Azure Virtual Machines Availability

6.Azure Virtual Machines Availability [Collection] Virtual Machines - Availability | Azure Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik

5.Create Virtual Machine Demo - Part 2 | Azure

5.Create Virtual Machine Demo - Part 2 | Azure [Collection] Create Virtual Machine Demo - Part 2 | Azure Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik

4.Create Virtual Machine Demo - Part 1 | Azure

4.Create Virtual Machine Demo - Part 1 | Azure [Collection] Create Virtual Machine Demo - Part 1 | Azure Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik

2. Azure Portal Explained

2. Azure Portal Explained [Collection] Azure Portal Explained Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik

1.Microsoft Azure Hands on Tutorial Series

1.Microsoft Azure Hands on Tutorial Series [Collection] Microsoft Azure Hands on Tutorial Series Hadoop, Machine & Deep Learning using Azure Cloud tutorial series - Nitin Kaushik

30.Deploy Flask App on Azure Explained

30.Deploy Flask App on Azure Explained [Collection] Deploy Flask App on Azure Explained Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik

3.Simulate Regression and Clustering data - Python

3.Simulate Regression and Clustering data - Python [Collection] Tutorials to read data either from MySQl, csv files or simulate data for Classification, Regression or Clustering algorithms using Python - Nitin Kaushik ***********Git Hub Repo for Source code********* https://github.com/nitinkaushik01/Various_ways_to_read_the_different_types_of_Data_Python

2. Simulate Classification Algorithm data using Python

2. Simulate Classification Algorithm data using Python [Collection] Tutorials to read data either from MySQl, csv files or simulate data for Classification, Regression or Clustering algorithms using Python - Nitin Kaushik ***********Git Hub Repo for Source code********* https://github.com/nitinkaushik01/Various_ways_to_read_the_different_types_of_Data_Python

1.Load Data using MySQL CSV file and from Scikit Learn package using Python

1.Load Data using MySQL CSV file and from Scikit Learn package using Python [Collection] Tutorials to read data either from MySQl, csv files or simulate data for Classification, Regression or Clustering algorithms using Python - Nitin Kaushik ***********Git Hub Repo for Source code********* https://github.com/nitinkaushik01/Various_ways_to_read_the_different_types_of_Data_Python

19.Elementwise operation using inline function

19.Elementwise operation using inline function [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

18.Mean Variance and Std Deviation of a matrix

18.Mean Variance and Std Deviation of a matrix [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

17.Convert dictionary into matrix

17.Convert dictionary into matrix [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

16.Dot product of two vectors

16.Dot product of two vectors [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

15.Rank of a matrix

15.Rank of a matrix [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

14.Trace of a matrix

14.Trace of a matrix [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

13.Flattening a matrix

13.Flattening a matrix [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

12.Determinant of a matrix

12.Determinant of a matrix [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

11.Find Max and Min element from Matrix

11.Find Max and Min element from Matrix [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

10.Diagonal of a matrix

10.Diagonal of a matrix [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

9.Invert a matrix

9.Invert a matrix [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

8.Reshape a matrix

8.Reshape a matrix [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

7. Select an element from vector and matrix

7. Select an element from vector and matrix [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

6.Dense to Sparse Matrix conversion

6.Dense to Sparse Matrix conversion [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

4.Matrix Addition and Subtraction

4.Matrix Addition and Subtraction [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

3.Matrix Size Shape and Dimension

3.Matrix Size Shape and Dimension [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

2.Create Matrix

2.Create Matrix [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

1.Create Vector

1.Create Vector [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

5.Matrix Transpose

5.Matrix Transpose [Collection] Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations

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

29.Deploy ML on Cloud - Push Image to Docker Hub

29.Deploy ML on Cloud - Push Image to Docker Hub [Collection] Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

28.Deploy ML on Cloud - Build Docker Image for Prediction App

28.Deploy ML on Cloud - Build Docker Image for Prediction App [Collection] Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

27.Deploy ML on Cloud - Build Requirements.txt file

27.Deploy ML on Cloud - Build Requirements.txt file [Collection] Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

26.Deploy ML on Cloud - Docker file for Flask app | Port Correction

26.Deploy ML on Cloud - Docker file for Flask app | Port Correction [Collection] Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

25.Deploy ML on Cloud - Docker file for Prediction Flask app

25.Deploy ML on Cloud - Docker file for Prediction Flask app [Collection] Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

24.Deploy ML on Cloud - Create Docker File

24.Deploy ML on Cloud - Create Docker File [Collection] Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

23.Deploy ML on Cloud - Run Docker Image

23.Deploy ML on Cloud - Run Docker Image [Collection] Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

22.Deploy ML on Cloud - Build Docker Images

22.Deploy ML on Cloud - Build Docker Images [Collection] Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

21.Deploy ML on Cloud - Docker Installation

21.Deploy ML on Cloud - Docker Installation [Collection] Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

20.How Docker works under the hood ?

20.How Docker works under the hood ? [Collection] How Docker works under the hood | Microsoft Azure Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

19.A primer on docker

19.A primer on docker [Collection] A primer on docker Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

18.Why Docker as a choice ?

18.Why Docker as a choice ? [Collection] Why Docker as a choice ? Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

17.Fix Errors on Flask app

17.Fix Errors on Flask app [Collection] Fix Errors on Flask app Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

16.Flask App Prediction Result Page

16.Flask App Prediction Result Page [Collection] Flask App Prediction Result Page Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

15.Flask App Prediction Homepage Part 3

15.Flask App Prediction Homepage Part 3 [Collection] Flask App Prediction Homepage Part 3 Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

14.Flask App Prediction Homepage Part 2

14.Flask App Prediction Homepage Part 2 [Collection] Flask App Prediction Homepage Part 2 Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

13.Flask App Prediction Homepage Part 1

13.Flask App Prediction Homepage Part 1 [Collection] Flask App Prediction Homepage Part 1 Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

12.Flask App Machine Learning Prediction Part 3

12.Flask App Machine Learning Prediction Part 3 [Collection] Flask App Machine Learning Prediction Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

11.Flask App Machine Learning Prediction Part 2

11.Flask App Machine Learning Prediction Part 2 [Collection] Flask App Machine Learning Prediction Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

10.Flask App Machine Learning Prediction Part 1

10.Flask App Machine Learning Prediction Part 1 [Collection] Flask App Machine Learning Prediction Part 1 Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

9.Flask Sample App Part 2

9.Flask Sample App Part 2 [Collection] Flask Sample App Part 2 Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

8.Flask Sample App Part 1

8.Flask Sample App Part 1 [Collection] Flask Sample App Part 1 Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

7.Multiple Linear Regression Python Code Part 3

7.Multiple Linear Regression Python Code Part 3 [Collection] Multiple Linear Regression Python Code Part 3 Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

6.Multiple Linear Regression Python Code Part 2

6.Multiple Linear Regression Python Code Part 2 [Collection] Multiple Linear Regression Python Code Part 2 Deploy Machine Learning Model using Flask Web App, Docker and Azure Cloud - Nitin Kaushik ***********Git Hub Repo for Source code, Pickle File****** https://github.com/nitinkaushik01/Deploy_Machine_Learning_Model_on_Flask_App

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

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

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