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

Grasp the fundamental concepts of deep learning using Tensorflow in a hands-on manner

Key Features

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

Book 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.

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

Who This Book Is 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.

Table of Contents

Data science: Bird's-eye view

Data Modeling in Action - The Titanic Example

Feature Engineering and Model Complexity - The Titanic Example Revisited

Get Up and Running with TensorFlow

Tensorflow in Action - Some Basic Examples

Deep Feed-forward Neural Networks - Implementing Digit Classification

Introduction to Convolutional Neural Networks

Object Detection - CIFAR-10 Example

Object Detection - Transfer Learning with CNNs

Recurrent-Type Neural Networks - Language modeling

Representation Learning - Implementing Word Embeddings

Neural sentiment Analysis

Autoencoders - Feature Extraction and Denoising

Generative Adversarial Networks in Action - Generating New Images

Face Generation and Handling Missing Labels

Appendix - Implementing Fish Recognition

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