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
1
Section 1: Core Concepts
3
Section 2: Computer Vision
8
Section 3: Natural Language and Sequence Processing
12
Section 4: A Look to the Future

Summary

In this chapter, we focused on seq2seq models and the attention mechanism. First, we discussed and implemented a regular recurrent encoder-decoder seq2seq model and learned how to complement it with the attention mechanism. Then, we talked about and implemented a purely attention-based type of model called a transformer. We also defined multihead attention in their context. Next, we discussed transformer language models (such as BERT, transformerXL, and XLNet). Finally, we implemented a simple text-generation example using the transformers library.

This chapter concludes our series of chapters with a focus on natural language processing. In the next chapter, we'll talk about some new trends in deep learning that aren't fully matured yet but hold great potential for the future.