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  • Book Overview & Buying Python Deep Learning
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Python Deep Learning

Python Deep Learning

By : Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter Roelants
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Python Deep Learning

Python Deep Learning

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

Overview of this book

With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you’ll find everything inside.
Table of Contents (17 chapters)
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1
Part 1:Introduction to Neural Networks
5
Part 2: Deep Neural Networks for Computer Vision
8
Part 3: Natural Language Processing and Transformers
13
Part 4: Developing and Deploying Deep Neural Networks

Language modeling

The goal of language models is to compute a probability of a sequence of words. They are crucial to a lot of different applications, such as speech recognition, optical character recognition, machine translation, and spelling correction. For example, in American English, the two phrases wreck a nice beach and recognize speech are almost identical in pronunciation, but their respective meanings are completely different from each other. A good language model can distinguish which phrase is most likely correct, based on the context of the conversation. This section will provide an overview of word- and character-level language models and how RNNs can be used to build them.

Word-based models

A word-based language model defines a probability distribution over sequences of words. Given a sequence of words of length m, it assigns a probability P(w 1 , ... , w m ) to the full sequence of words. The application of these probabilities are two-fold. We can use them to estimate the...

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Python Deep Learning
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