In Chapter 7, Understanding Recurrent Networks, we outlined several types of recurrent models, depending on the input-output combinations. One of them is indirect many-to-many or sequence-to-sequence (seq2seq), where an input sequence is transformed into another, different output sequence, not necessarily with the same length as the input. Machine translation is the most popular type of seq2seq task. The input sequences are the words of a sentence in one language and the output sequences are the words of the same sentence translated into another language. For example, we can translate the English sequence tourist attraction to the German touristenattraktion. Not only is the output sentence a different length, but there is no direct correspondence between the elements of the input and output sequences. In particular, one output element...
Advanced Deep Learning with Python
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Advanced Deep Learning with Python
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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)
Preface
The Nuts and Bolts of Neural Networks
Section 2: Computer Vision
Understanding Convolutional Networks
Advanced Convolutional Networks
Object Detection and Image Segmentation
Generative Models
Section 3: Natural Language and Sequence Processing
Language Modeling
Understanding Recurrent Networks
Sequence-to-Sequence Models and Attention
Section 4: A Look to the Future
Emerging Neural Network Designs
Meta Learning
Deep Learning for Autonomous Vehicles
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