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

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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...

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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Steven Feb 19, 2019

This book provides a great introduction to deep and reinforcement learning. First, It does a good job at explaining in detail the basics of neural networks. Then, it gradually introduces more complex models like convolutional and recurrent networks in an easy to understand way.The computer vision section is comprehensive and has a good mix between theoretical and practical knowledge - especially the parts about residual networks, object detection, and generative networks.The chapter about natural language processing is good, but tries to introduce a lot of material in little space. It would have been better for the explanations to be more detailed, especially the attention models and speech recognition parts.It's interesting that the book also includes an introduction to reinforcement learning - it serves as a good basis for further research in this field.The chapter about autonomous vehicles deserves an honorable mention as well. It's rare to find this topic in other books.

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Georgi Petrov Feb 19, 2019

I do like the added history of AI and Machine learning, it makes it well clear what the differences are and how we often use the terms interchangeably. Even though it says "second edition" you don't need the first book. This book is also a good read for those who are not too deep into Deep Learning(pun intended), contains practical examples and the methodology is well explained. I am still new to the subject but the book was engaging with great practical examples which I was able to follow to the best of my skills. Highly recommend for any skill levels, it's a good motivation to boost up your technical skills.

Tobias Bockhorst Mar 26, 2020

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.

Lily Apr 06, 2019

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).

L Aug 07, 2020

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.

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

What do you get with Print?

Python Deep Learning

Neural Networks

The need for neural networks

An introduction to neural networks

Training neural networks

Summary

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

What do you get with Print?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

Packt Subscriptions

Frequently bought together

Table of Contents

Recommendations for you

Customer reviews

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About the 5 authors

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