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Deep Learning Essentials

By : Di, Jianing Wei, Anurag Bhardwaj

3.1 (7)

Buy this Book

Deep Learning Essentials

3.1 (7)

By: Di, Jianing Wei, Anurag Bhardwaj

Buy this Book

Overview of this book

Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as CNN, RNN, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing using Python library such as TensorFlow. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, and small datasets. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.

Preface

Who this book is for

What this book covers

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Free Chapter

Why Deep Learning?

What is AI and deep learning?

The history and rise of deep learning

Why deep learning?

The motivation of deep architecture

Applications

Future potential and challenges

Summary

Getting Yourself Ready for Deep Learning

Basics of linear algebra

Deep learning with GPU

Deep learning software frameworks

Setting up deep learning on AWS

Summary

Getting Started with Neural Networks

Multilayer perceptrons

How a network learns

Deep learning models

Practical examples

Summary

Deep Learning in Computer Vision

Origins of CNNs

Convolutional Neural Networks

Fine-tuning CNNs

Popular CNN architectures

Summary

NLP - Vector Representation

Traditional NLP

Deep learning NLP

Applications

Summary

Advanced Natural Language Processing

Deep learning for text

Recurrent neural networks

Long short-term memory network

Applications

Summary

Multimodality

What is multimodality learning?

Challenges of multimodality learning

Image captioning

Visual question answering

Multi-source based self-driving

Summary

Deep Reinforcement Learning

What is reinforcement learning (RL)?

Deep reinforcement learning

Implementing reinforcement learning

Summary

Deep Learning Hacks

Massaging your data

Tricks in training

Fine-tuning

Model compression

Summary

Deep Learning Trends

Recent models for deep learning

Novel applications

Summary

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Deep learning with GPU

As the name suggests, deep learning involves learning a deeper representation of data, which requires large amounts of computational power. Such massive computational power is usually not possible with modern day CPUs. GPUs, on the other hand, lend themselves very nicely to this task. GPUs were originally designed for rendering graphics in real time. The design of a typical GPU allows for the disproportionately larger number of arithmetic logical unit (ALU), which allows them to crunch a large number of calculations in real time.

GPUs used for general purpose computation have a high data parallel architecture, which means they can process a large number of data points in parallel, leading to higher computational throughput. Each GPU is composed of thousands of cores. Each of such cores consists of a number of functional units which contain cache and ALU...