What's New In TensorFlow 2.x?

Table of contents

1. Preface
2. 1. What Is TensorFlow?
   1. What Is TensorFlow?
      1. On Tensors, Dimensions, Ranks, Orders, Matrices, and Vectors
      2. Element-Wise Addition
      3. Element-Wise Multiplication
      4. Vector Dot Product
      5. Matrix-Vector Product
      6. Matrix-Matrix Product
      7. Understanding the Axes Parameter
   2. Implementing Machine Learning Algorithms with TensorFlow
      1. Linear Regression Using TensorFlow
      2. Training a Model Using TensorFlow
      3. Neural Networks Using TensorFlow
   3. Keras
   4. Summary
3. 2. Eager Execution
   1. Python: The De Facto Standard in Data Science
   2. Why Eager Execution Is So Important
   3. Eager Execution in Practice
   4. Operator and Method Overloading
   5. Integration of NumPy
   6. Summary
4. 3. tf.function and AutoGraph
   1. Understanding tf.function
   2. AutoGraph
   3. Summary
5. 4. Keras on TensorFlow 2.x
   1. Keras Versus TensorFlow Linear Algebra Code
   2. Model Compilation
   3. Keras Backward Compatibility
   4. Functional Versus Sequential API
   5. Custom Layers
      1. Lambda Layers
      2. Real Custom Layers
   6. Keras Applications
   7. Summary
6. 5. Parallel Neural Network Training
   1. Parallel Neural Network Training Explained
      1. Inter-Model Parallelism
      2. Data Parallelism
      3. Intra-Model Parallelism
      4. Pipelined Parallelism
   2. Data Parallelism in TensorFlow
      1. MirroredStrategy
      2. CentralStorageStrategy
      3. MultiWorkerMirroredStrategy
      4. TPUStrategy
      5. ParameterServerStrategy
   3. Data Parallelism in Keras
   4. Running on Multiple GPUs on Multiple Servers
   5. Summary
7. 6. TensorBoard
   1. What Is TensorBoard?
   2. Launching TensorBoard
      1. Command Line
      2. In a Notebook
      3. Notable Differences in the New API
   3. Basic Visualizations
      1. Scalars
      2. Graphs
      3. Distributions and Histograms
      4. Images
      5. Visualizing Other Images
      6. Others
   4. Embeddings Visualizer
      1. In TensorBoard
      2. In Embeddings Projector
      3. Understanding the Visualization
      4. Axes Projections
      5. Loading Your Own Data
   5. TensorBoard Profiler
      1. The Overview Page
      2. Input Pipeline Analyzer
      3. (GPU) Kernel Stats
      4. TensorFlow Stats
      5. Trace Viewer
   6. The HParams Dashboard in TensorBoard
   7. Summary
8. 7. Other New Features of TensorFlow 2.x
   1. API Cleanup
   2. No More Globals
   3. SavedModel, TensorFlow Serving, TensorFlow Lite, TensorFlow.js, TensorFlow Hub
      1. TensorFlow Serving
      2. TensorFlow.js
      3. TensorFlow Lite
      4. TensorFlow Hub
   4. tf.data
      1. tf.data.Dataset Is Iterable
      2. Creating tf.data.Dataset from CSV Data
      3. Data Preprocessing
   5. The Estimator API and Premade Estimators
   6. Summary
9. 8. Conclusion
10. A. Introduction to Deep Learning
    1. Understanding Deep Learning
       1. The Perceptron
       2. The Single Hidden Layer Network
       3. Deep Learning Networks
    2. Summary