Book Image

Deep Learning Essentials

By : Wei Di, Jianing Wei, Anurag Bhardwaj

3 (1)

Book Image

Deep Learning Essentials

3 (1)

By: Wei Di, Jianing Wei, Anurag Bhardwaj

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

To get the most out of this book

Free Chapter

Why Deep Learning?

Why Deep Learning?

What is AI and deep learning?

The history and rise of deep learning

Why deep learning?

The motivation of deep architecture

Future potential and challenges

Getting Yourself Ready for Deep Learning

Getting Yourself Ready for Deep Learning

Basics of linear algebra

Deep learning with GPU

Deep learning software frameworks

Setting up deep learning on AWS

Getting Started with Neural Networks

Getting Started with Neural Networks

Multilayer perceptrons

How a network learns

Deep learning models

Practical examples

Deep Learning in Computer Vision

Deep Learning in Computer Vision

Origins of CNNs

Convolutional Neural Networks

Fine-tuning CNNs

Popular CNN architectures

NLP - Vector Representation

NLP - Vector Representation

Traditional NLP

Deep learning NLP

Advanced Natural Language Processing

Advanced Natural Language Processing

Deep learning for text

Recurrent neural networks

Long short-term memory network

Multimodality

What is multimodality learning?

Challenges of multimodality learning

Image captioning

Visual question answering

Multi-source based self-driving

Deep Reinforcement Learning

Deep Reinforcement Learning

What is reinforcement learning (RL)?

Deep reinforcement learning

Implementing reinforcement learning

Deep Learning Hacks

Deep Learning Hacks

Massaging your data

Tricks in training

Model compression

Deep Learning Trends

Deep Learning Trends

Recent models for deep learning

Novel applications

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Preface

Deep learning is the most disruptive trend in the tech world, having jumped out of research laboratories right into production environments. It is the science and art of working through hidden layers of data to get deep insights. Deep learning is currently one of the best providers of solutions to problems in image recognition, speech recognition, object recognition, and natural language processing (NLP).

We’ll start off by brushing up on machine learning and quickly get into the fundamentals of deep learning and its implementation. Moving on, we’ll teach you about the different types of neural networks and their applications in the real world. With the help of insightful examples, you’ll learn to recognize patterns using a deep neural network and get to know other important concepts such as data manipulation and classification.

Using the reinforcement learning technique with deep learning, you’ll build AI that can outperform any human and also work with the LSTM network. During the course of this book, you will come across a wide range of different frameworks and libraries, such as TensorFlow, Python, Nvidia, and others. By the end of the book, you’ll be able to deploy a production-ready deep learning framework for your own applications.