by Radhika Datar | Released February 2019 | ISBN: 9781789957198

Understanding TensorFlow 2.0's new featuresAbout This VideoNew features and functionality explained simply and easily via videos.Code demos and examples help you understand how the new features workUnderstand the differences between TensorFlow 1.x and 2.0, and how they're compatibleIn DetailTensorFlow is a popular and widely-adopted open-source Machine Learning library. It is Python-friendly and used in various AI areas such as deep learning, numeric computation, and large-scale Machine Learning. The newest version of TensorFlow includes major highlights such as improved eager execution, improved compatibility, support for major platforms and languages, and more; it also removes deprecated APIs. The new features will make TensorFlow easier to learn and apply.In this course, you will cover all of the new features that have been introduced in TensorFlow 2.0 especially the major highlight including Eager Execution and more. You will learn how to make the best use of these features and how it improves and simplifies the way you use TensorFlow.By the end of the course, you will have an understanding of the new features introduced in TensorFlow 2.0 and will be able to apply them in your work. Show and hide more Publisher resources Download Example Code

1. Chapter 1 : Introduction to TensorFlow 2.0
   • The Course Overview 00:02:49
   • Tensorflow 2.0 00:02:42
   • Understanding Differences between TensorFlow 1.x and 2.0 00:03:46
   • Maintaining Continuity and Compatibility between Version 1.x and 2.0 00:03:41
2. Chapter 2 : Model Building with Data
   • Understanding Model Building in TensorFlow 00:06:07
   • Problem Statement 00:05:20
   • Evaluating the Model 00:05:37
   • Analyzing the Model 00:03:50
   • Distribution API 00:05:33
3. Chapter 3 : Training and Validating the Model
   • Introduction to Training Data for Deep Learning 00:01:23
   • Downloading and Preparing the Data 00:03:15
   • Evaluating the Data Model with Eager Execution 00:03:07
   • Nearest Neighbor Algorithm and Data Clustering 00:03:10
4. Chapter 4 : Debugging and Execution of Graphs
   • Introduction to TensorBoard 00:02:21
   • Understanding Graphs and Sessions 00:05:20
   • Demonstrating a Simple Graph 00:08:26
   • Graph Visualization 00:07:39
5. Chapter 5 : Export Feature to Saved Model
   • Creating Custom Estimators 00:02:17
   • Evaluation and Prediction 00:03:28
   • Datasets for Estimators 00:05:16
   • Feature Columns 00:04:12
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