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

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

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內容描述

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.

目錄大綱

Part 1 Introduction to AI

1. Introduction
2. Artificial Intelligence
3. History of Neural Networks
4. Characteristics of Deep Learning
5. Applications of Deep Learning
6. Deep Learning Frameworks
7. Installation of Development Environment
8. Regression
   2.1 Neuron Model
   2.2 Optimization Methods
   2.3 Hands-on Linear Models
   2.4 Linear Regression
9. 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
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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 ResNet1

作者介紹

​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.