Deep Learning By Example

Key Features

• 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.

Book Description

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.

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.