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Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands of online learners. 
You'll start with an introduction to AI, where you'll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you'll jump into simple classification programs for hand-writing analysis. Once you've tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you'll get into the heavy lifting of programming neural networks  and working with a wide variety of neural network types such as GANs and RNNs.  
Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer!      

What You'll Learn

• Develop using deep learning algorithms
  
• Build deep learning models using TensorFlow 2
  
• Create classification systems and other, practical deep learning applications
  

Who This Book Is For
Students, programmers, and researchers with no experience in deep learning who want to build up their basic skillsets. Experienced machine learning programmers and engineers might also find value in updating their skills.

Beginning Deep Learning with TensorFlow: Work with Keras, MNIST Data Sets, and Advanced Neural Networks

Part 1 Introduction to AI 

1. Introduction

1.    Artificial Intelligence

2.    History of Neural Networks

3.    Characteristics of Deep Learning

4.    Applications of Deep Learning

5.    Deep Learning Frameworks

6.    Installation of Development Environment

2.             Regression

2.1      Neuron Model

2.2 Optimization Methods

2.3 Hands-on Linear Models

2.4 Linear Regression 

3.             Classification

3.1 Hand-writing Digital Picture Dataset

3.2 Build a Classification Model

3.3 Compute the Error

3.4 Is the Problem Solved?

3.5 Nonlinear Model

3.6 Model Representation Ability

3.7 Optimization Method

3.8 Hands-on Hand-written Recognition

3.9 Summary

Part 2 Tensorflow 

4.             Tensorflow 2 Basics

4.1 Datatype

4.2 Numerical Precision

4.3 What is a Tensor?

4.4 Create a Tensor

4.5 Applications of Tensors

4.6 Indexing and Slicing

4.7 Dimension Change

4.8 Broadcasting

4.9 Mathematical Operations

4.10 Hands-on Forward Propagation Algorithm

5.             Tensorflow 2 Pro

5.1 Aggregation and Seperation

5.2 Data Statistics

5.3 Tensor Comparison

5.4 Fill and Copy

5.5 Data Clipping

5.6 High-level Operations

5.7 Load Classic Datasets

5.8 Hands-on MNIST Dataset Practice

Part 3 Neural Networks

6.             Neural Network Introduction

6.1 Perception Model

6.2 Fully-Connected Layers

6.3 Neural Networks

6.4 Activation Functions

6.5 Output Layer

6.6 Error Calculation

6.7 Neural Network Categories

6.8 Hands-on Gas Consuming Prediction

7.             Backpropagation Algorithm

7.1 Derivative and Gradient

7.2 Common Properties of Derivatives

7.3 Derivatives of Activation Functions

7.4 Gradient of Loss Function

7.5 Gradient of Fully-Connected Layers

7.6 Chain Rule

7.7 Back Propagation Algorithm

7.8 Hands-on Himmelblau Function Optimization

7.9 Hands-on Back Propagation Algorithm

8.             Keras Basics

8.1 Basic Functionality

8.2 Model Configuration, Training and Testing

8.3 Save and Load Models

8.4 Customized Class

8.5 Model Zoo

8.6 Metrics

8.7 Visualization

9.             Overfitting

9.1 Model Capability

9.2 Overfitting and Underfitting

9.3 Split the Dataset

9.4 Model Design

9.5 Regularization

9.6 Dropout

9.7 Data Enhancement

9.8 Hands-on Overfitting

Part 4 Deep Learning Applications

10.          Convolutional Neural Network

10.1 Problem of Fully-Connected Layers

10.2 Convolutional Neural Network

10.3 Convolutional Layer

10.4 Hands-on LeNet-5

10.5 Representation Learning

10.6 Gradient Propagation

10.7 Pooling Layer

10.8 BatchNorm Layer

10.9 Classical Convolutional Neural Network

10.10 Hands-on CIFRA10 and VGG13

10.11 Variations of Convolutional Neural Network

10.12 Deep Residual Network

10.13 DenseNet

10.14 Hands-on CIFAR10 and ResNet18

11.          Recurrent Neural Network

11.1 Time Series 

11.2 Recurrent Neural Network (RNN)

11.3 Gradient Propagation

11.4 RNN Layer

11.5 Hands-on RNN Sentiment Classification

11.6 Gradient Vanishing and Exploding

11.7 RNN Short Memory

11.8 LSTM Principle

11.9 LSTM Layer

11.10 GRU Basics

11.11 Hands-on Sentiment Classification with LSTM/GRU

11.12 Pre-trained Word Vectors

12.          Auto-Encoders

12.1 Basics of Auto-Encoders

12.2 Hands-on Reconstructing MNIST Pictures

12.3 Variations of Auto-Encoders

12.4 Variational Auto-Encoders (VAE) 

12.5 Hands-on VAE

13.           Generative Adversarial Network (GAN)

13.1 Examples of Game Theory

13.2 GAN Basics

13.3 Hands-on DCGAN

13.4 Variants of GAN

13.5 Nash Equilibrium

13.6 Difficulty of Training GAN

13.7 WGAN Principle

13.8 Hands-on WGAN-GP

14.          Reinforcement Learning

14.1 Introduction 

14.2 Reinforcement Learning Problem

14.3 Policy Gradient Method

14.4 Metric Function Method

14.5 Actor-Critic Method

14.6 Summary

15.          Custom Dataset Pipeline

15.1 Pokémon Go Dataset

15.2 Load Customized Dataset

15.3 Hands-on Pokémon Go Dataset

15.4 Transfer Learning

15.5 Save Model

15.6 Model Deployment

Audience: Beginner to Intermediate