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Python Deep Learning
Python Deep Learning

Python Deep Learning: Next generation techniques to revolutionize computer vision, AI, speech and data analysis

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Profile Icon Zocca Profile Icon Spacagna Profile Icon Daniel Slater Profile Icon Roelants
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$9.99 $47.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (10 Ratings)
eBook Apr 2017 406 pages 1st Edition
eBook
$9.99 $47.99
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$60.99
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Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Zocca Profile Icon Spacagna Profile Icon Daniel Slater Profile Icon Roelants
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$9.99 $47.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (10 Ratings)
eBook Apr 2017 406 pages 1st Edition
eBook
$9.99 $47.99
Paperback
$60.99
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Free Trial
Renews at $19.99p/m
eBook
$9.99 $47.99
Paperback
$60.99
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Renews at $19.99p/m

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Python Deep Learning

Chapter 2. Neural Networks

In the previous chapter, we described several machine learning algorithms and we introduced different techniques to analyze data to make predictions. For example, we suggested how machines can use data of home selling prices to make predictions on the price for new houses. We described how large companies, such as Netflix, use machine learning techniques in order to suggest to users new movies they may like based on movies they have liked in the past, using a technique that is widely utilized in e-commerce by giants such as Amazon or Walmart. Most of these techniques, however, necessitate labeled data in order to make predictions on new data, and, in order to improve their performance, need humans to describe the data in terms of features that make sense.

Humans are able to quickly extrapolate patterns and infer rules without having the data cleaned and prepared for them. It would then be desirable if machines could learn to do the same. As we have discussed...

Why neural networks?

Neural networks have been around for many years, and they have gone through several periods during which they have fallen in and out of favor. However, in recent years, they have steadily gained ground over many other competing machine learning algorithms. The reason for this is that advanced neural net architecture has shown accuracy in many tasks that has far surpassed that of other algorithms. For example, in the field of image recognition, accuracy may be measured against a database of 16 million images named ImageNet.

Prior to the introduction of deep neural nets, accuracy had been improving at a slow rate, but after the introduction of deep neural networks, accuracy dropped from an error rate of 40% in 2010 to less than 7% in 2014, and this value is still falling. The human recognition rate is still lower, but only at about 5%. Given the success of deep neural networks, all entrants to the ImageNet competition in 2013 used some form of deep neural network. In...

Fundamentals

In the first chapter, we talked about three different approaches to machine learning: supervised learning, unsupervised learning, and reinforcement learning. Classical neural networks are a type of supervised machine learning, though we will see later that deep learning popularity is instead due to the fact that modern deep neural networks can be used in unsupervised learning tasks as well. In the next chapter, we will highlight the main differences between classical shallow neural networks and deep neural nets. For now, however, we will mainly concentrate on classical feed-forward networks that work in a supervised way. Our first question is, what exactly is a neural network? Probably the best way to interpret a neural network is to describe it as a mathematical model for information processing. While this may seem rather vague, it will become much clearer in the next chapters. A neural net is not a fixed program, but rather a model, a system that processes information, or...

Summary

In this chapter, we have introduced neural networks in detail and we have mentioned their success over other competing algorithms. Neural networks are comprised of the "units", or neurons, that belong to them or their connections, or weights, that characterize the strength of the communication between different neurons and their activity functions, that is, how the neurons process the information. We have discussed how we can create different architectures, and how a neural network can have many layers, and why inner (hidden) layers are important. We have 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 activity function defined, and finally we have shown how we can define a method called back-propagation to "tune" the weights to improve the desired level of accuracy. We have also mentioned many of the areas where neural networks are and have been employed.

In the next chapter...

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Key benefits

  • Explore and create intelligent systems using cutting-edge deep learning techniques
  • Implement deep learning algorithms and work with revolutionary libraries in Python
  • Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and more

Description

With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you’ll find everything inside.

Who is this book for?

This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired.

What you will learn

  • Get a practical deep dive into deep learning algorithms
  • Explore deep learning further with Theano, Caffe, Keras, and TensorFlow
  • Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines
  • Dive into Deep Belief Nets and Deep Neural Networks
  • Discover more deep learning algorithms with Dropout and Convolutional Neural Networks
  • Get to know device strategies so you can use deep learning algorithms and libraries in the real world

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Publication date : Apr 28, 2017
Length: 406 pages
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Language : English
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Publication date : Apr 28, 2017
Length: 406 pages
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Table of Contents

11 Chapters
1. Machine Learning – An Introduction Chevron down icon Chevron up icon
2. Neural Networks Chevron down icon Chevron up icon
3. Deep Learning Fundamentals Chevron down icon Chevron up icon
4. Unsupervised Feature Learning Chevron down icon Chevron up icon
5. Image Recognition Chevron down icon Chevron up icon
6. Recurrent Neural Networks and Language Models Chevron down icon Chevron up icon
7. Deep Learning for Board Games Chevron down icon Chevron up icon
8. Deep Learning for Computer Games Chevron down icon Chevron up icon
9. Anomaly Detection Chevron down icon Chevron up icon
10. Building a Production-Ready Intrusion Detection System Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.1
(10 Ratings)
5 star 70%
4 star 10%
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1 star 20%
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Gloria Kwan Sep 24, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book covers all the important subjects in deep learning, including convolutional networks, auto-encoders, RNNs and reinforcement learning. I loved the way all subjects are introduced with lots of examples that help the intuition. Chapter 1 gives a quick summary of all the major machine learning techniques, and chapter 2 quickly shows how Deep Learning differs from most machine learning techniques. Chapter 3 covers feature learning introducing Ising models and this is something I have seen rarely done, but it helps understand how hidden layers work, and why auto-encoders can be such powerful tools. This, by itself, is extremely valuable and it is worth the price of the book, and I have very rarely seen it covered so well. Chapter 4 introduces auto-encoders and restricted boltzmann machines in a rigorous mathematical way. In chapter 5 convolutional networks are explained and examples are given of vey powerful neural network examples for image recognition. Chapter 6 provides great coverage of RNN and language recognition. Chapter 7 and Chapter 8 touch on a lot of subjects like reinforcement learning, montecarlo searches and q-learning. This is where a lot of research is being done now, and the technical level of these chapters is excellent. Finally chapter 9 and 10 introduce H2O and anomaly detection and end up with covering all the important element for deploying a production-ready system. I also love the fact that all major libraries are used throughout the book: TensorFlow, Theano, H2O and Keras, which helps the reader work with different libraries. The technical level of both the coverage of the theory and the code examples is first class, and it is something I have rarely seen in a book with such a large breadth of subjects covered. If one wants to start learning and reading about Deep Learning, this is now the first book I would recommend, but I think that even experienced readers can still learn quite a lot from it.
Amazon Verified review Amazon
Amazon Customer Sep 15, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good book
Amazon Verified review Amazon
Kindle Customer Jun 15, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book, it walks you throgh the basics of machine learning, all the way throughout deep learning and its applications. It provides great examples for NN/Deep Learning such as computer games and intrusion detection. I highly recommend this book A+++++
Amazon Verified review Amazon
Carissa Gallo Aug 26, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Found this very easy to follow and well written. I'd been looking for reading on this topic for a while and was surprised at the low availability overall. If you have an interest in deep learning, I can't recommend this book enough.
Amazon Verified review Amazon
who the hell am I? May 05, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I started to read this book with great interest, and I have not been disappointed. The first chapters offer a great introduction with lots of intuitive examples, and they help tremendously to understand the ideas behind deep learning. The language is clear and aimed both at the beginner as it is at the expert. The book covers all the important topics on deep learning and it provides a lot of code examples. I am extremely happy with it.EDIT: After finishing this book (at the time of the review I had only read the first 3 chapters) I am still quite happy. The book has a very nice balance of intuition and solid math. I was surprised to read the two negative reviews (by the way, written on the same day by someone who has no other reviews) and they seemed fake to me. This book covers every important subject in Deep Learning with plenty of examples from the basic theory to the final chapter that covers use of H2o in a production system, and does so in a very consistent manner.
Amazon Verified review Amazon
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