Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch
Authors: Ivan Vasilev
ISBN-10: 178995617X
ISBN-13: 9781789956177
Publication Date 出版日期: 2019-12-12
Publisher finelybook 出版社: Packt Publishing
Print Length 页数: 468 pages
Book Description
By finelybook
Gain expertise in advanced deep learning domains such as neural networks,meta-learning,graph neural networks,and memory augmented neural networks using the Python ecosystem
In order to build robust deep learning systems,you’ll need to understand everything from how neural networks work to training CNN models. In this book,you’ll discover newly developed deep learning models,methodologies used in the domain,and their implementation based on areas of application.
You’ll start by understanding the building blocks and the math behind neural networks,and then move on to CNNs and their advanced applications in computer vision. You’ll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on,you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words,before going on to cover various types of recurrent networks,such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later,you’ll use graph neural networks for processing structured data,along with covering meta-learning,which allows you to train neural networks with fewer training samples. Finally,you’ll understand how to apply deep learning to autonomous vehicles.
By the end of this book,you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.
What you will learn
Cover advanced and state-of-the-art neural network architectures
Understand the theory and math behind neural networks
Train DNNs and apply them to modern deep learning problems
Use CNNs for object detection and image segmentation
Implement generative adversarial networks (GANs) and variational autoencoders to generate new images
Solve natural language processing (NLP) tasks,such as machine translation,using sequence-to-sequence models
Understand DL techniques,such as meta-learning and graph neural networks
Contents
Preface
Section 1: Core Concepts
Chapter 1: The Nuts and Bolts of Neural Networks
Section 2: Computer Vision
Chapter 2: Understanding Convolutional Networks
Chapter 3: Advanced Convolutional Networks
Chapter 4: Object Detection and Image Segmentation
Chapter 5: Generative Models
Section 3: Natural Language and Sequence Processing
Chapter 6: Language Modeling
Chapter 7: Understanding Recurrent Networks
Chapter 8: Sequence-to-Sequence Models and Attention
Section 4: A Look to the Future
Chapter 9: Emerging Neural Network Designs
Chapter 10: Meta Learning
Chapter 11: Deep Learning for Autonomous Vehicles
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Index
