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

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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.

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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Amazon verified reviews

Gloria Kwan Sep 24, 2017

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 Customer Sep 15, 2017

Good book

Kindle Customer Jun 15, 2017

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+++++

Carissa Gallo Aug 26, 2017

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.

who the hell am I? May 05, 2017

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.

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

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