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Advanced Deep Learning with Python

By : Vasilev

4.8 (23)

Buy this Book

Advanced Deep Learning with Python

4.8 (23)

By: Vasilev

Buy this Book

Overview of this book

In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.

Preface

Who this book is for

What this book covers

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Section 1: Core Concepts

The Nuts and Bolts of Neural Networks

The mathematical apparatus of NNs

A short introduction to NNs

Training NNs

Summary

Section 2: Computer Vision

Understanding Convolutional Networks

Understanding CNNs

Introducing transfer learning

Summary

Advanced Convolutional Networks

Introducing AlexNet

An introduction to Visual Geometry Group

Understanding residual networks

Understanding Inception networks

Introducing Xception

Introducing MobileNet

An introduction to DenseNets

The workings of neural architecture search

Introducing capsule networks

Summary

Object Detection and Image Segmentation

Introduction to object detection

Introducing image segmentation

Summary

Generative Models

Intuition and justification of generative models

Introduction to VAEs

Introduction to GANs

Types of GAN

Introducing artistic style transfer

Summary

Section 3: Natural Language and Sequence Processing

Language Modeling

Understanding n-grams

Introducing neural language models

Implementing language models

Summary

Understanding Recurrent Networks

Introduction to RNNs

Introducing long short-term memory

Introducing gated recurrent units

Implementing text classification

Summary

Sequence-to-Sequence Models and Attention

Introducing seq2seq models

Seq2seq with attention

Understanding transformers

Transformer language models

Summary

Section 4: A Look to the Future

Emerging Neural Network Designs

Introducing Graph NNs

Introducing memory-augmented NNs

Summary

Meta Learning

Introduction to meta learning

Metric-based meta learning

Optimization-based learning

Summary

Deep Learning for Autonomous Vehicles

Introduction to AVs

Components of an AV system

Introduction to 3D data processing

Imitation driving policy

Driving policy with ChauffeurNet

Summary

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Introducing long short-term memory

Hochreiter and Schmidhuber studied the problems of vanishing and exploding gradients extensively and came up with a solution called Long Short-Term Memory (LSTM, https://www.bioinf.jku.at/publications/older/2604.pdf). LSTMs can handle long-term dependencies due to a specially crafted memory cell. In fact, they work so well that most of the current accomplishments in training RNNs on a variety of problems are due to the use of LSTMs. In this section, we'll explore how this memory cell works and how it solves the vanishing gradients issue.

The key idea of LSTM is the cell state, c_t (in addition to the hidden RNN state, h_t), where the information can only be explicitly written in or removed so that the state stays constant if there is no outside interference. The cell state can only be modified by specific gates, which are a way to let information...