Book Image

Python Deep Learning - Second Edition

By : Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
Book Image

Python Deep Learning - Second Edition

By: Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca

Overview of this book

With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.
Table of Contents (12 chapters)

Sequence to sequence learning

Many, many NLP problems can be formulated as sequence to sequence tasks. This is a type of task where an input sequence is transformed into another, different output sequence, not necessarily with the same length as the input. To better understand this concept, let's look at some examples:

  • 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 there is no direct correspondence between the elements of the input and output sequences. In particular, one output element corresponds to a combination...