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Python Deep Learning

Python Deep Learning

By : Vasilev, Daniel Slater, Spacagna, Roelants, Zocca
4 (8)
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Python Deep Learning

Python Deep Learning

4 (8)
By: Vasilev, Daniel Slater, Spacagna, Roelants, 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 (16 chapters)
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Title Page
About Packt
Contributors
Preface
Index

Applications of deep learning


Machine learning in general, and deep learning in particular, are producing more and more astonishing results in terms of the quality of predictions, feature detection, and classification. Many of these recent results have made the news. Such is the pace of progress, that some experts are worrying that machines will soon be more intelligent than humans. But I hope that any such fears you might have will be alleviated after you have read this book. For better or worse, we're still far from human-level intelligence. 

In Chapter 2, Neural Networks, we mentioned how deep learning algorithms have occupied the leaderboard of the ImageNet competition. They are successful enough to make the jump from academia to industry. Let's now talk about some real-world use cases of deep learning:

  • Nowadays, new cars have a suite of safety and convenience features that aim to make the driving experience safer and less stressful. One such feature is automated emergency braking if the...

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