## Book description

**Numerical computing, data processing, and enough about deep learning to get you up and running**

**About This Book**

Get your first experience with deep learning with this easy-to-follow guide

Implement neural networks with the easiest, developer-friendly tools and techniques in the market.

**Who This Book Is For**

This book is dedicated to developers, data analysts, or deep learning enthusiasts who do not have much background with complex numerical computations but want to know what is deep learning. The book majorly appeals to beginners who are looking for a quick guide to gain some hands-on experience with deep learning. Some experience with Python would be great.

**What You Will Learn**

Learn about Data Science, its challenges and how to tackle them.

Learn the basics of Data Science and modern best practices with a Titanic Example.

Get familiarized with one of the most powerful platforms for Deep Learning(DL), TensorFlow 1.x.

Basic of Deep Learning and modern best practices with a digit classification problem of MNIST.

Dive into imaging problems by looking at early lung cancer detection and emotion recognition using CNN.

Apply deep learning to other domains like Language Modeling, ChatBots and Machine Translation using the one of the powerful architectures of DL, RNN.

**In Detail**

Deep Learning has made some huge and significant contributions and it’s one of the mostly adopted techniques in order to drive insights from your data nowadays. Google developed one of the most used libraries (aka. TensorFlow) to use in order to build fast, robust against an error-prone and scale deep learning algorithms that can run on both CPU and GPU.

This book is a starting point for those who are keen on knowing about deep learning and implementing it, but do not have extensive background in machine learning. We will start with introducing you with Data science for performing data analysis, machine learning, and eventually deep learning. Then, you will explore algorithms and various techniques that lead into efficient data processing. You will learn to clean, mine, and analyze data. Once you are comfortable with some analysis, you will then move to creating machine learning models that will eventually lead you to neural networks. You will get familiar with basics of deep learning and explore various tools that enable deep learning in a powerful yet user friendly manner. While all of this is being taught, spread across the book, we will be using intuitive examples like Titanic survivor prediction, Housing price predictor, etc. teaching implementations of each of the concept. With a very low starting point, this book will enable a regular developer to get hands on experience with deep learning.

By the end of this book, you will learn all the essentials needed to explore and understand what is deep learning and will perform deep learning tasks first hand.

Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.

## Table of contents

- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Preface
- Data Science - A Birds' 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
- Face Generation and Handling Missing Labels
- Implementing Fish Recognition
- Other Books You May Enjoy

## Product information

- Title: Deep Learning By Example
- Author(s):
- Release date: February 2018
- Publisher(s): Packt Publishing
- ISBN: 9781788399906

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