What Is the Goal?

What Will I Learn from This Book?

The TF 1.x and TF 2.0 Books: How Are They Different?

Why Isn’t Keras in Its Own Chapter in This Book?

How Much Keras Knowledge Is Needed for This Book?

Do I Need to Learn the Theory Portions of This Book?

How Were the Code Samples Created

What Are the Technical Prerequisites for This Book?

What Are the Nontechnical Prerequisites for This Book?

Which Topics Are Excluded?

How Do I Set Up a Command Shell?

Companion Files

What Are the “Next Steps” after Finishing This Book?

Chapter 1: Introduction to TensorFlow 2

What Is TF 2?

TF 2 Use Cases

TF 2 Architecture: The Short Version

TF 2 Installation

TF 2 and the Python REPL

Other TF 2-Based Toolkits

TF 2 Eager Execution

TF 2 Tensors, Data Types, and Primitive Types

TF 2 Data Types

TF 2 Primitive Types

Constants in TF 2

Variables in TF 2

The tf.rank() API

The tf.shape() API

Variables in TF 2 (Revisited)

TF 2 Variables versus Tensors

What Is @tf.function in TF 2?

How Does @tf.function Work?

A Caveat about @tf.function in TF 2

The tf.print() Function and Standard Error

Working with @tf.function in TF 2

An Example without @tf.function

An Example with @tf.function

Overloading Functions with @tf.function

What Is AutoGraph in TF 2?

Arithmetic Operations in TF 2

Caveats for Arithmetic Operations in TF 2

TF 2 and Built-In Functions

Calculating Trigonometric Values in TF

Calculating Exponential Values in TF 2

Working with Strings in TF 2

Working with Tensors and Operations in TF 2

Second-Order Tensors in TF 2 (1)

Second-Order Tensors in TF 2 (2)

Multiplying Two Second-Order Tensors in TF

Convert Python Arrays to TF Tensors

Conflicting Types in TF 2

Differentiation and tf.GradientTape in TF 2

Examples of tf.GradientTape

Using the watch() Method of tf.GradientTape

Using Nested Loops with tf.GradientTape

Other Tensors with tf.GradientTape

A Persistent Gradient Tape

Migrating TF 1.x Code to TF 2 Code (optional)

Two Conversion Techniques from TF 1.x to TF 2

Converting to Pure TF 2 Functionality

Converting Sessions to Functions

Combine tf.data.Dataset and @tf.function

Use Keras Layers and Models to Manage Variables

The TensorFlow Upgrade Script (optional)

Summary

Chapter 2: Useful TF 2 APIs

TF 2 Tensor Operations

Using for Loops in TF 2

Using while Loops in TF 2

TF 2 Operations with Random Numbers

TF 2 Tensors and Maximum Values

The TF 2 range() API

Operations with Nodes

The tf.size(), tf.shape(), and tf.rank() APIs

The tf.reduce_prod() and tf.reduce_sum() APIs

The tf.reduce_mean() API

The tf.random_normal() API (1)

The TF 2 random_normal() API (2)

The tf.truncated_normal() API

The tf.reshape() API

The tf.range() API

The tf.equal() API (1)

The tf.equal() API (2)

The tf.argmax() API (1)

The tf.argmax() API (2)

The tf.argmax() API (3)

Combining tf.argmax() and tf.equal() APIs

Combining tf.argmax() and tf.equal() APIs (2)

The tf.map_fn() API

What Is a One-Hot Encoding?

The TF one_hot() API

Other Useful TF 2 APIs

Save and Restore TF 2 Variables

TensorFlow Ragged Constants and Tensors

What Is a TFRecord?

A Simple TFRecord

What Are tf.layers?

What Is TensorBoard?

TF 2 with TensorBoard

TensorBoard Dashboards

The tf.summary API

Google Colaboratory

Other Cloud Platforms

Gcp Sdk

Summary

Chapter 3: TF2 Datasets

The TF 2 tf.data.Datasets

Creating a Pipeline

Basic Steps for TF 2 Datasets

A Simple TF 2 tf.data.Dataset

What Are Lambda Expressions?

Working with Generators in TF 2

What Are Iterators? (optional)

TF 1.x Iterators (optional)

Concatenating TF 2 tf.Data.Datasets

The TF 2 reduce() Operator

Working with Generators in TF 2

The TF 2 filter() Operator (1)

The TF 2 filter() Operator (2)

The TF 2 batch() Operator (1)

The TF 2 batch() Operator (2)

The TF 2 map() Operator (1)

The TF 2 map() Operator (2)

The TF 2 flatmap() Operator (1)

The TF 2 flatmap() Operator (2)

The TF 2 flat_map() and filter() Operators

The TF 2 repeat() Operator

The TF 2 take() Operator

Combining the TF 2 map() and take() Operators

Combining the TF 2 zip() and batch() Operators

Combining the TF 2 zip() and take() Operators

TF 2 tf.data.Datasets and Random Numbers

TF 2, MNIST, and tf.data.Dataset

Working with the TFDS Package in TF 2

The CIFAR10 Dataset and TFDS in TF 2

Working with tf.estimator

What Are TF 2 Estimators?

Other TF 2 Namespaces

Summary

Chapter 4: Linear Regression

What Is Linear Regression?

Linear Regression versus Curve-Fitting

What Is Multivariate Analysis?

When Are Solutions Exact in Machine Learning?

Challenges with Linear Regression

Nonlinear Data

Nonconstant Variance of Error Terms

Correlation of Error Terms

Collinearity

Outliers and Anomalies

Other Types of Regression

Working with Lines in the Plane

Scatter Plots with NumPy and Matplotlib (1)

Why the “Perturbation Technique” Is Useful

Scatter Plots with NumPy and Matplotlib (2)

A Quadratic Scatter Plot with NumPy and Matplotlib

The Mean Squared Error (MSE) Formula

A List of Error Types

Nonlinear Least Squares

What Is Regularization?

Machine Learning and Feature Scaling

Data Normalization vs. Standardization

The Bias-Variance Trade-off

Metrics for Measuring Models

Limitations of R-Squared

Confusion Matrix

Accuracy vs. Precision vs. Recall

Other Useful Statistical Terms

What Is an F1 Score?

What Is a p-value?

Working with Datasets

Training Data Versus Test Data

What Is Cross-Validation?

Calculating the MSE Manually

Simple 2D Data Points in TF 2

TF2, tf.GradientTape(), and Linear Regression

Working with Keras

Working with Keras Namespaces in TF 2

Working with the tf.keras.layers Namespace

Working with the tf.keras.activations Namespace

Working with the tf.keras.datasets Namespace

Working with the tf.keras.experimental Namespace

Working with Other tf.keras Namespaces

TF 2 Keras versus “Standalone” Keras

Creating a Keras-Based Model

Keras and Linear Regression

Working with tf.estimator

Summary

Chapter 5: Working with Classifiers

What Is Classification?

What Are Classifiers?

Common Classifiers

What Are Linear Classifiers?

What Is KNN?

How to Handle a Tie in kNN

What Are Decision Trees?

What Are Random Forests?

What Are SVMS?

Trade-offs of SVMs

What Is Bayesian Inference?

Bayes’s Theorem

Some Bayesian Terminology

What Is MAP?

Why Use Bayes’s Theorem?

What Is a Bayesian Classifier?

Types of Naive Bayes Classifiers

Training Classifiers

Evaluating Classifiers

What Are Activation Functions?

Why Do We Need Activation Functions?

How Do Activation Functions Work?

Common Activation Functions

Activation Functions in Python

The ReLU and ELU Activation Functions

The Advantages and Disadvantages of ReLU

ELU

Sigmoid, Softmax, and Hardmax Similarities

Softmax

Softplus

Tanh

Sigmoid, Softmax, and Hardmax Differences

TF 2 and the Sigmoid Activation Function

What Is Logistic Regression?

Setting a Threshold Value

Logistic Regression: Assumptions

Linearly Separable Data

TensorFlow and Logistic Regression

Keras and Early Stopping (1)

Keras and Early Stopping (2)

Keras and Metrics

Distributed Training in TF 2 (Optional)

Using tf.distribute.Strategy with Keras

Summary

What Is Deep Learning?

What Are Hyperparameters?

Deep Learning Architectures

Problems That Deep Learning Can Solve

Challenges in Deep Learning

What Are Perceptrons?

Definition of the Perceptron Function

A Detailed View of a Perceptron

The Anatomy of an Artificial Neural Network (ANN)

The Model Initialization Hyperparameters

The Activation Hyperparameter

The Cost Function Hyperparameter

The Optimizer Hyperparameter

The Learning Rate Hyperparameter

The Dropout Rate Hyperparameter

What Is Backward Error Propagation?

What Is a Multilayer Perceptron (MLP)?

Activation Functions

How Are Data Points Correctly Classified?

Keras and the XOR Function

A High-Level View of CNNs

A Minimalistic CNN

The Convolutional Layer (Conv2D)

The ReLU Activation Function

The Max Pooling Layer

CNNs with Audio Signals

CNNs and NLPs

Displaying an Image in the MNIST Dataset

Keras and the MNIST Dataset

Keras, CNNs, and the MNIST Dataset

What Is an RNN?

Anatomy of an RNN

What Is BPTT?

Working with RNNs and TF 2

What Is an LSTM?

Anatomy of an LSTM

Bidirectional LSTMs

LSTM Formulas

LSTM Hyperparameter Tuning

What Are GRUs?

What Are Autoencoders?

Autoencoders and PCA

What Are Variational Autoencoders?

What Are GANs?

The VAE-GAN Model

Working with NLP (Natural Language Processing)

NLP Techniques

The Transformer Architecture and NLP

Transformer-XL Architecture

NLP and Deep Learning

NLP and Reinforcement Learning

Data Preprocessing Tasks

Popular NLP Algorithms

What Is an n-Gram?

What Is a Skip-Gram?

What Is BoW?

What Is Term Frequency?

What Is Inverse Document Frequency (idf)?

What Is tf-idf?

What Are Word Embeddings?

ELMo, ULMFit, OpenAI, and BERT

What Is Translatotron?

What Is Reinforcement Learning (RL)?

What Are NFAs?

What Are Markov Chains?

Markov Decision Processes (MDPs)

The Epsilon-Greedy Algorithm

The Bellman Equation

Other Important Concepts in RL

RL Toolkits and Frameworks

TF-Agents

What Is Deep Reinforcement Learning (DRL)?

Miscellaneous Topics

TFX (TensorFlow Extended)

TensorFlow Probability

TensorFlow Graphics

TF Privacy

Summary

TensorFlow 2 Pocket Primer: A Quick Reference Guide for TensorFlow 2 Developers

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TensorFlow 2 Pocket Primer: A Quick Reference Guide for TensorFlow 2 Developers

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Description

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What you will learn

Product Details

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Product Details

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Table of Contents

Recommendations for you

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FAQs