Book Image

Advanced Deep Learning with Python

By : Ivan Vasilev
Book Image

Advanced Deep Learning with Python

By: Ivan Vasilev

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.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
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1
Section 1: Core Concepts
Section 1: Core Concepts
2
The Nuts and Bolts of Neural Networks
The Nuts and Bolts of Neural Networks
The mathematical apparatus of NNs
A short introduction to NNs
Training NNs
Summary
3
Section 2: Computer Vision
Section 2: Computer Vision
4
Understanding Convolutional Networks
Understanding Convolutional Networks
Understanding CNNs
Introducing transfer learning
Summary
5
Advanced Convolutional Networks
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
6
Object Detection and Image Segmentation
Object Detection and Image Segmentation
Introduction to object detection
Introducing image segmentation
Summary
7
Generative Models
Generative Models
Intuition and justification of generative models
Introduction to VAEs
Introduction to GANs
Types of GAN
Introducing artistic style transfer
Summary
8
Section 3: Natural Language and Sequence Processing
Section 3: Natural Language and Sequence Processing
9
Language Modeling
Language Modeling
Understanding n-grams
Introducing neural language models
Implementing language models
Summary
10
Understanding Recurrent Networks
Understanding Recurrent Networks
Introduction to RNNs
Introducing long short-term memory
Introducing gated recurrent units
Implementing text classification
Summary
11
Sequence-to-Sequence Models and Attention
Sequence-to-Sequence Models and Attention
Introducing seq2seq models
Seq2seq with attention
Understanding transformers
Transformer language models
Summary
12
Section 4: A Look to the Future
Section 4: A Look to the Future
13
Emerging Neural Network Designs
Emerging Neural Network Designs
Introducing Graph NNs
Introducing memory-augmented NNs
Summary
14
Meta Learning
Meta Learning
Introduction to meta learning
Metric-based meta learning
Optimization-based learning
Summary
15
Deep Learning for Autonomous Vehicles
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
16
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Understanding n-grams

A word-based language model defines a probability distribution over sequences of words. Given a sequence of words of length m (for example, a sentence), it assigns a probability P(w1, ... , wm) to the full sequence of words. We can use these probabilities as follows:

  • To estimate the likelihood of different phrases in NLP applications.
  • As a generative model to create new text. A word-based language model can compute the likelihood of a given word following a sequence of words.

The inference of the probability of a long sequence, say w1, ..., wm, is typically infeasible. We can calculate the joint probability of P(w1, ... , wm) with the chain rule of joint probability (Chapter 1, The Nuts and Bolts of Neural Networks):

The probability of the later words given the earlier words would be especially difficult to estimate from the data. That's why this...

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