Description
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. Yoll start with an introduction to AI, where yoll 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, yoll jump into simple classification programs for hand-writing analysis. Once yove 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, yoll 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. Liangqu Long is a well-known deep learning educator and engineer in China. He is a successfully published author in the topic area with years of experience in teaching machine learning concepts. His two online video tutorial courses “Deep Learning with PyTorch” and “Deep Learning with TensorFlow 2” have received massive positive comments and allowed him to refine his deep learning teaching methods. Xiangming Zeng is an experienced data scientist and machine learning practitioner. He has over ten years of experience using machine learning and deep learning models to solve real world problems in both academia and professionally. Xiangming is familiar with deep learning fundamentals and mainstream machine learning libraries such as Tensorflow and scikit-learn. 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 Pokmon Go Dataset 15.2 Load Customized Dataset 15.3 Hands-on Pokmon Go Dataset 15.4 Transfer Learning 15.5 Save Model 15.6 Model Deployment Audience: Beginner to Intermediate
