Deep Learning By Example

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

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Deep Learning By Example

Ahmed Menshawy

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

Quick links: > What will you learn?> Table of content

Access cutting-edge content as it's created

Want access to this book right now? Read as we develop it as part of our Early Access program. Click here to find out more about Early Access.

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Book Details

ISBN 139781788399906
Paperback635 pages

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.

Table of Contents

Chapter 1: Introduction
Understanding data science by an example
Getting to learning
Design procedure of data science algorithms
Different learning types
Data size and industry needs
Summary
Chapter 2: Getting Started with Data Science: Titanic Example
Chapter 3: Model Complexity and Assessment: Titanic Example
Chapter 4: Deep Feedforward Neural Network: Digit Classification
Chapter 5: DL First Steps with TensorFlow
Chapter 6: TensorFlow: Extending on the Housing Example
Chapter 7: Deep Feedforward Neural Network: Digit Classification
Chapter 8: Deep Learning Best Practices: Digit Classification Revisited
Chapter 9: Feature Extraction and Dataset Denoising: Digit Classification
Chapter 10: Convolutional Neural Network: Lung Cancer Detection
Chapter 11: Transfer Learning: Lung Cancer Detection Revisited
Chapter 12: Emotion Recognition
Chapter 13: Language Modeling
Chapter 14: Building Chatbots
Chapter 15: English-German Chatbot

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.

Authors

Table of Contents

Chapter 1: Introduction
Understanding data science by an example
Getting to learning
Design procedure of data science algorithms
Different learning types
Data size and industry needs
Summary
Chapter 2: Getting Started with Data Science: Titanic Example
Chapter 3: Model Complexity and Assessment: Titanic Example
Chapter 4: Deep Feedforward Neural Network: Digit Classification
Chapter 5: DL First Steps with TensorFlow
Chapter 6: TensorFlow: Extending on the Housing Example
Chapter 7: Deep Feedforward Neural Network: Digit Classification
Chapter 8: Deep Learning Best Practices: Digit Classification Revisited
Chapter 9: Feature Extraction and Dataset Denoising: Digit Classification
Chapter 10: Convolutional Neural Network: Lung Cancer Detection
Chapter 11: Transfer Learning: Lung Cancer Detection Revisited
Chapter 12: Emotion Recognition
Chapter 13: Language Modeling
Chapter 14: Building Chatbots
Chapter 15: English-German Chatbot

Book Details

ISBN 139781788399906
Paperback635 pages
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