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.
Table of Contents (17 chapters)
Free Chapter
Section 1: Core Concepts
Section 2: Computer Vision
Section 3: Natural Language and Sequence Processing
Section 4: A Look to the Future

Section 1: Core Concepts

This section will discuss some core Deep Learning (DL) concepts: what exactly DL is, the mathematical underpinnings of DL algorithms, and the libraries and tools that make it possible to develop DL algorithms rapidly.

This section contains the following chapter:

  • Chapter 1, The Nuts and Bolts of Neural Networks