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Deep Learning

MIT Press Essential Knowledge Series

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About this listen

Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.

Kelleher explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning-major trends, possible developments, and significant challenges.

©2019 Massachusetts Institute of Technology (P)2019 Gildan Media Computer Science Machine Theory & Artificial Intelligence Technology Machine Learning Data Science Artificial Intelligence Programming
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The content of the book and performance of the reader was great but the book contains a lot of formulas and plots and therefore a pdf would be a great addition to make it a five star book.

Great book but needs a pdf for formulas

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Very well-written but not a natural fit for an audiobook. It's essentially a book heavily dependent on mathematical expressions and neural network diagrams. The narrator makes a valiant attempt to describe diagrams, math, etc in words but, in the end, I bought the Kindle version to check my understanding. I found it stimulating to try and follow the audio math but it took some heavy-duty concentration.
Overall, I'd describe it as both excellent and hard work. It would greatly benefit from a pdf for the diagrams and math expressions.

Excellent content but needs a pdf

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The author clearly explains all the concepts and ideas in a way that is approachable to non-specialists.

There are elements that don’t work brilliantly in this format (ie equations) but this isn’t a criticism of the book per se.

Short, easy to follow and to the point—it is a job well done.

Comprehensive but not overly detailed

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the book is well written with explanation that allows you to imagine the workings of deep learning models.

Imagination

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