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Key benefits

Get a first-hand experience of the deep learning concepts and techniques with this easy-to-follow guide

Train different types of neural networks using Tensorflow for real-world problems in language processing, computer vision, transfer learning, and more

Designed for those who believe in the concept of 'learn by doing', this book is a perfect blend of theory and code examples

Description

Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence.

Who is this book for?

This book targets data scientists and machine learning developers who wish to get started with deep learning. If you know what deep learning is but are not quite sure of how to use it, this book will help you as well. An understanding of statistics and data science concepts is required. Some familiarity with Python programming will also be beneficial.

What you will learn

Understand the fundamentals of deep learning and how it is different from machine learning

Get familiarized with Tensorflow, one of the most popular libraries for advanced machine learning

Increase the predictive power of your model using feature engineering

Understand the basics of deep learning by solving a digit classification problem of MNIST

Demonstrate face generation based on the CelebA database, a promising application of generative models

Apply deep learning to other domains like language modeling, sentiment analysis, and machine translation

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Lifeng Han Apr 02, 2018

This book has a very large coverage of content and knowledge. It covers the Data Modeling, Feature Engineering and Model Complexity, currently popular DL toolkit TensorFlow with examples, and different kinds of Neural Nets such as Feed-forward NN/CNN/GAN with some application introduction like classification task, sentiment analysis and face generation. It also introduces transfer learning and representation learning.I do believe the audiences will benefit from this advanced machine learning book with examples.

Amazon Verified review

Tom Francart Jul 17, 2018

While I was really looking forward to reading this book, I'm disappointed on two fronts:1. Typesetting: I normally wouldn't even mention this, but it's really bad. There are a lot of code examples, which are made very hard to read by improper line wrapping. Also, there is no syntax highlighting, which would have been helpful. In addition, the formulas look terrible: the font is not matched with the remainder of the text and the resolution is low.In addition, the figures are printed in black and white, but are not legible in black and white, for instance the text refers to "blue" and "red".On the whole, this book looks as if someone quickly converted it from another format and didn't even bother to look at the end result. This is unacceptable for a book produced by a supposedly professional company.2. Content: the references are unscientific; wikipedia and random websites are not reliable sources of information, especially for definitions that are essential to the book.Another disappointment is that the author describes how a certain DNN can be implemented in Tensorflow, but he does not explain how this DNN (in particular the hyperparameters) was obtained. I was hoping to learn by example how to find a good architecture and optimise the parameters, which is the hard part of deep learning.

Deep Learning By Example: A hands-on guide to implementing advanced machine learning algorithms and neural networks

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