This book will start off by introducing the foundations of machine learning, what makes learning visible, demonstrating the traditional machine learning techniques with some examples and eventually deep learning. You will then move to creating machine learning models that will eventually lead you to neural networks. You will get familiar with the basics of deep learning and explore various tools that enable deep learning in a powerful yet user-friendly manner. With a very low starting point, this book will enable a regular developer to get hands-on experience with deep learning. You will learn all the essentials needed to explore and understand what deep learning is and will perform deep learning tasks first-hand. Also, we will be using one of the most widely used deep learning frameworks. TensorFlow has big community support that is growing day by day, which makes it a good option for building your complex deep learning applications.
Deep Learning By Example
Deep Learning By Example
Overview of this book
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.
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Current Title:
Deep Learning By Example
Table of Contents (18 chapters)
Preface
Free Chapter
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
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