Hands-On Deep Learning Architectures with Python

Create deep neural networks to solve computational problems using TensorFlow and Keras

Yuxi (Hayden) Liu , Saransh Mehta 지음

Packt(GCO Science) 2019년 04월 30일 출간

작품소개

책소개

▶What You Will Learn
- Implement CNNs, RNNs, and other commonly used architectures with Python
- Explore architectures such as VGGNet, AlexNet, and GoogLeNet
- Build deep learning architectures for AI applications such as face and image recognition, fraud detection, and many more
- Understand the architectures and applications of Boltzmann machines and autoencoders with concrete examples
- Master artificial intelligence and neural network concepts and apply them to your architecture
- Understand deep learning architectures for mobile and embedded systems

▶Key Features
- Explore advanced deep learning architectures using various datasets and frameworks
- Implement deep architectures for neural network models such as CNN, RNN, GAN, and many more
- Discover design patterns and different challenges for various deep learning architectures

▶Who This Book Is For
If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book

목차

▶TABLE of CONTENTS
1 Getting Started with Deep Learning
2 Deep Feedforward Networks
3 Restricted Boltzmann Machines and Autoencoders
4 CNN Architecture
5 Mobile Neural Networks and CNNs
6 Recurrent Neural Networks
7 Generative Adversarial Networks
8 New Trends of Deep Learning

책 속으로

▶What this book covers
- Chapter 1, Getting Started with Deep Learning, covers the evolution of intelligence in machines and artificial intelligence and, eventually, deep learning. We'll then look at some applications of deep learning and set up our environment for coding our way through deep learning models. Completing this chapter, you will learn the following things.

- Chapter 2, Deep Feedforward Networks, covers the evolution history of deep feedforward networks and their architecture. We will also demonstrate how to bring up and preprocess data for training a deep learning network.

- Chapter 3, Restricted Boltzmann Machines and Autoencoders, explains the algorithm behind the scenes, called restricted Boltzmann machines (RBMs) and their evolutionary path. We will then dig deeper into the logic behind them and implement RBMs in TensorFlow. We will also apply them to build a movie recommender. We'll then learn about autoencoders and briefly look at their evolutionary path. We will also illustrate a variety of autoencoders, categorized by their architectures or forms of regularization.

- Chapter 4, CNN Architecture, covers an important class of deep learning network for images, called convolutional neural networks (CNNs). We will also discuss the benefits of CNNs over deep feedforward networks. We will then learn more about some famous image classification CNNs and then build our first CNN image classifier on the CIFAR-10 dataset. Then, we'll move on to object detection with CNNs and the TensorFlow detection model, zoo.

- Chapter 5, Mobile Neural Networks and CNNs, discusses the need for mobile neural networks for doing CNN work in a real-time application. We will also talk about the two benchmark MobileNet architectures introduced by Google―MobileNet and MobileNetV2. Later, we'll discuss the successful combination of MobileNet with object detection networks such as SSD to achieve object detection on mobile devices.

- Chapter 6, Recurrent Neural Networks, explains one of the most important deep learning models, recurrent neural networks (RNNs), its architecture, and the evolutionary path of RNNs. Later, we'll will discuss a variety of architectures categorized by the recurrent layer, including vanilla RNNs, LSTM, GRU, and bidirectional RNNs, and apply the vanilla architecture to write our own War and Peace (a bit nonsensical though). We'll also introduce the bidirectional architecture that allows the model to preserve information from both past and future contexts of the sequence.

- Chapter 7, Generative Adversarial Networks, explains one of the most interesting deep learning models, generative adversarial networks (GANs), and its evolutionary path. We will also illustrate a variety of GAN architectures with an example of image generation. We will also explore four GAN architectures, including vanilla GANs, deep convolutional GANs, conditional GANs, and information-maximizing GANs.

- Chapter 8, New Trends in Deep Learning, talks about a few deep learning ideas that we have found impactful this year and more prominent in the future. We'll also learn that Bayesian deep learning combines the merits of both Bayesian learning and deep learning.

출판사 서평

▶ Preface
Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems.

Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more―all with practical implementations.

By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world.

작가정보

저자(글) Yuxi (Hayden) Liu

Yuxi (Hayden) Liu is an author of a series of machine learning books and an education enthusiast. His first book, the first edition of Python Machine Learning By Example, was a #1 bestseller on Amazon India in 2017 and 2018 and his other book R Deep Learning Projects, both published by Packt Publishing.He is an experienced data scientist who is focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his machine learning expertise to computational advertising, recommendations, and network anomaly detection. He published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto.

저자(글) Saransh Mehta

Saransh Mehta has cross-domain experience of working with texts, images, and audio using deep learning. He has been building artificial, intelligence-based solutions, including a generative chatbot, an attendee-matching recommendation system, and audio keyword recognition systems for multiple start-ups. He is very familiar with the Python language, and has extensive knowledge of deep learning libraries such as TensorFlow and Keras. He has been in the top 10% of entrants to deep learning challenges hosted by Microsoft and Kaggle.