Book Image

Python Deep Learning - Second Edition

By : Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
Book Image

Python Deep Learning - Second Edition

By: Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca

Overview of this book

With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.

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Python Deep Learning - Second Edition
Ivan Vasilev | Daniel Slater | Gianmario Spacagna | Peter Roelants | Valentino Zocca
Jan, 2019 | 386 pages
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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
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1
Machine Learning - an Introduction
Machine Learning - an Introduction
Introduction to machine learning
Different machine learning approaches
Neural networks
Summary
2
Neural Networks
Neural Networks
The need for neural networks
An introduction to neural networks
Training neural networks
Summary
3
Deep Learning Fundamentals
Deep Learning Fundamentals
Introduction to deep learning
Fundamental deep learning concepts
Deep learning algorithms
Applications of deep learning
The reasons for deep learning's popularity
Introducing popular open source libraries
Summary
4
Computer Vision with Convolutional Networks
Computer Vision with Convolutional Networks
Intuition and justification for CNN
Convolutional layers
Stride and padding in convolutional layers
Pooling layers
The structure of a convolutional network
Improving the performance of CNNs
A CNN example with Keras and CIFAR-10
Summary
5
Advanced Computer Vision
Advanced Computer Vision
Transfer learning
Advanced network architectures
Capsule networks
Advanced computer vision tasks
Artistic style transfer
Summary
6
Generating Images with GANs and VAEs
Generating Images with GANs and VAEs
Intuition and justification of generative models
Variational autoencoders
Generative Adversarial networks
Summary
7
Recurrent Neural Networks and Language Models
Recurrent Neural Networks and Language Models
Recurrent neural networks
Language modeling
Sequence to sequence learning
Speech recognition
Summary
8
Reinforcement Learning Theory
Reinforcement Learning Theory
RL paradigms
RL as a Markov decision process
Finding optimal policies with Dynamic Programming
Monte Carlo methods
Temporal difference methods
Value function approximations
Experience replay
Q-learning in action
Summary
9
Deep Reinforcement Learning for Games
Deep Reinforcement Learning for Games
Introduction to genetic algorithms playing games
Deep Q-learning
Policy gradient methods
Model-based methods
Summary
10
Deep Learning in Autonomous Vehicles
Deep Learning in Autonomous Vehicles
Brief history of AV research
AV introduction
Imitiation driving policy
Driving policy with ChauffeurNet
DL in the Cloud
Summary
11
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Model-based methods

RL methods such as Monte Carlo, SARSA, Q-learning, or Actor-Critic are model-free. The main goal of the agent is to learn an (imperfect) estimation of either the true value function (MC, SARSA, Q-learning) or the optimal policy (AC). As the learning goes on, the agent needs to have a way to explore the environment in order to collect experiences for its training. Usually, this happens with trial and error. For example, an ε-greedy policy will take random actions at certain times, just for the sake of environment exploration.

In this section, we'll introduce model-based RL methods, where the agent won't follow the trial-and-error approach when it takes new actions. Instead, it will plan the new action with the help of a model of the environment. The model will try to simulate how the environment will react to a given action. Then, the agent...

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