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Python Deep Learning
Python Deep Learning

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow , Second Edition

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Profile Icon Vasilev Profile Icon Roelants Profile Icon Spacagna Profile Icon Zocca Profile Icon Daniel Slater +1 more Show less
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Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (8 Ratings)
Paperback Jan 2019 386 pages 2nd Edition
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Profile Icon Vasilev Profile Icon Roelants Profile Icon Spacagna Profile Icon Zocca Profile Icon Daniel Slater +1 more Show less
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Paperback Jan 2019 386 pages 2nd Edition
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Python Deep Learning

Neural Networks

In Chapter 1, Machine Learning – an Introduction, we introduced a number of basic machine learning(ML) concepts and techniques. We went through the main ML paradigms, as well as some popular classic ML algorithms, and we finished with neural networks. In this chapter, we will formally introduce what neural networks are, describe in detail how a neuron works, see how we can stack many layers to create a deep feedforward neural network, and then we'll learn how to train them.

In this chapter, we will cover the following topics:

  • The need for neural networks
  • An introduction to neural networks
  • Training neural networks
Initially, neural networks were inspired by the biological brain (hence the name). Over time, however, we've stopped trying to emulate how the brain works and instead we focused on finding the correct configurations for specific tasks...

The need for neural networks

Neural networks have been around for many years, and they've gone through several periods during which they've fallen in and out of favor. But recently, they have steadily gained ground over many other competing machine learning algorithms. This resurgence is due to having computers that are fast, the use of graphical processing units (GPUs) versus the most traditional use of computing processing units (CPUs), better algorithms and neural net design, and increasingly larger datasets that we'll see in this book. To get an idea of their success, let's take the ImageNet Large-Scale Visual Recognition Challenge (http://image-net.org/challenges/LSVRC/, or just ImageNet). The participants train their algorithms using the ImageNet database. It contains more than one million high-resolution color images in over a thousand categories (one...

An introduction to neural networks

We can describe a neural network as a mathematical model for information processing. As discussed in Chapter 1, Machine Learning – an Introduction, this is a good way to describe any ML algorithm, but, in this chapter, well give it a specific meaning in the context of neural networks. A neural net is not a fixed program, but rather a model, a system that processes information, or inputs. The characteristics of a neural network are as follows:

  • Information processing occurs in its simplest form, over simple elements called neurons.
  • Neurons are connected and they exchange signals between them through connection links.
  • Connection links between neurons can be stronger or weaker, and this determines how information is processed.
  • Each neuron has an internal state that is determined by all the incoming connections from other neurons.
  • Each neuron...

Training neural networks

We have seen how neural networks can map inputs onto determined outputs, depending on fixed weights. Once the architecture of the neural network has been defined and includes the feed forward network, the number of hidden layers, the number of neurons per layer, and the activation function, we'll need to set the weights, which, in turn, will define the internal states for each neuron in the network. First, we'll see how to do that for a 1-layer network using an optimization algorithm called gradient descent, and then we'll extend it to a deep feed forward network with the help of backpropagation.

The general concept we need to understand is the following:

Every neural network is an approximation of a function, so each neural network will not be equal to the desired function, but instead will differ by some value called error. During training...

Summary

In this chapter, we introduced neural networks in detail and we mentioned their success vis-à-vis other competing algorithms. Neural networks are comprised of interconnected neurons (or units), where the weights of the connections characterize the strength of the communication between different neurons. We discussed different network architectures, and how a neural network can have many layers, and why inner (hidden) layers are important. We explained how the information flows from the input to the output by passing from each layer to the next based on the weights and the activation function, and finally, we showed how to train neural networks, that is, how to adjust their weights using gradient descent and backpropagation.

In the next chapter, we'll continue discussing deep neural networks, and we'll explain in particular the meaning of deep in deep learning...

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Key benefits

  • Build a strong foundation in neural networks and deep learning with Python libraries
  • Explore advanced deep learning techniques and their applications across computer vision and NLP
  • Learn how a computer can navigate in complex environments with reinforcement learning

Description

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.

Who is this book for?

This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

What you will learn

  • Grasp the mathematical theory behind neural networks and deep learning processes
  • Investigate and resolve computer vision challenges using convolutional networks and capsule networks
  • Solve generative tasks using variational autoencoders and Generative Adversarial Networks
  • Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models
  • Explore reinforcement learning and understand how agents behave in a complex environment
  • Get up to date with applications of deep learning in autonomous vehicles

Product Details

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Publication date : Jan 16, 2019
Length: 386 pages
Edition : 2nd
Language : English
ISBN-13 : 9781789348460
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Product Details

Publication date : Jan 16, 2019
Length: 386 pages
Edition : 2nd
Language : English
ISBN-13 : 9781789348460
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Languages :
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Tools :

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Table of Contents

11 Chapters
Machine Learning - an Introduction Chevron down icon Chevron up icon
Neural Networks Chevron down icon Chevron up icon
Deep Learning Fundamentals Chevron down icon Chevron up icon
Computer Vision with Convolutional Networks Chevron down icon Chevron up icon
Advanced Computer Vision Chevron down icon Chevron up icon
Generating Images with GANs and VAEs Chevron down icon Chevron up icon
Recurrent Neural Networks and Language Models Chevron down icon Chevron up icon
Reinforcement Learning Theory Chevron down icon Chevron up icon
Deep Reinforcement Learning for Games Chevron down icon Chevron up icon
Deep Learning in Autonomous Vehicles Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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(8 Ratings)
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1 star 12.5%
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L Aug 07, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
As mentioned in the book, the book is for people with some basic understandings of machine learning. It's not a beginner book.The language is concise and easy to understand. The book provides a pretty comprehensive roadmap/summary to learn deep learnings and provides links for most of the landmark research papers.The printing quality can be further improved.The github repository link provided in the book points to the repo for a different book. You need to search for the first author's git repo for the python codes.
Amazon Verified review Amazon
Tobias Bockhorst Mar 26, 2020
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Despite the fact that I've seen good publications by the authors previously,this particular one is somewhat improvable with respect to graphics quality,albeit some aspects here (e.g. contrast) may be somewhat related to PACKTpublishing as I've encountered rather poor graphics quality in some of theirtitles before.
Amazon Verified review Amazon
Ao-Ling Jan 09, 2020
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This is a very comprehensive book about major models of deep learning.
Amazon Verified review Amazon
Grant Moule Jun 07, 2019
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This book is a good summary and introduction to deep learning with Python. More useful for those that have not been working long with deep learning and would like an introduction.
Amazon Verified review Amazon
Lily Apr 06, 2019
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I like the topic selection, which covers virtually all major deep learning advancements in recent years starting from neural net basics and going all the way to reinforcement learning and autonomous vehicles. I also like that 3 of the major deep learning libraries are covered - it makes it easier for novices to compare them and understand their differences. A minor improvement would be to include jypiter notebooks in the code repository. I think this book can benefit beginners, because of the wide range of covered topics. It can also help more experienced people who want to improve your knowledge in some specific area (in my case GANs and reinforcement learning).
Amazon Verified review Amazon
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