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Summary

An interactive deep learning book with code, math, and discussions. Provides both NumPy/MXNet and PyTorch implementations. “We set out to create a resource that could (i) be freely available for everyone; (ii) offer sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist; (iii) include runnable code, showing readers how to solve problems in practice; (iv) allow for rapid updates, both by us and also by the community at large; and (v) be complemented by a forum for interactive discussion of technical details and to answer questions”.


Contents

Preface
Installation
Notation

  1. Introduction
  2. Preliminaries
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  3. Linear Neural Networks
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  4. Multilayer Perceptrons
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  5. Deep Learning Computation
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  6. Convolutional Neural Networks
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  7. Modern Convolutional Neural Networks
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  8. Recurrent Neural Networks
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  9. Modern Recurrent Neural Networks
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  10. Attention Mechanisms
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  11. Optimization Algorithms
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  12. Computational Performance
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  13. Computer Vision
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  14. Natural Language Processing: Pretraining
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  15. Natural Language Processing: Applications
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  16. Recommender Systems
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  17. Generative Adversarial Networks
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  18. Appendix: Mathematics for Deep Learning
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  19. Appendix: Tools for Deep Learning

Authors

Aston Zhang. Amazon Senior Scientist. (Source: d2l.ai/index.html).

undefinedZack C. Lipton.
Amazon Scientist
CMU Assistant Professor.(Source: d2l.ai/index.html).

undefinedMu Li.
Amazon Principal Scientist. (Source: d2l.ai/index.html).

undefinedAlex J. Smola.
Amazon VP/Distinguished Scientist, (Source: d2l.ai/index.html).

Chapter Authors

Brent Werness.
Amazon Research Scientist
Mathematics for Deep Learning. (Source: d2l.ai/index.html).

Rachel Hu.
Amazon Applied Scientist
Mathematics for Deep Learning.(Source: d2l.ai/index.html). (Source: d2l.ai/index.html).

Shuai Zhang.
Postdoctoral Researcher at ETH Zürich
Recommender Systems.(Source: d2l.ai/index.html).

undefinedYi Tay.
Google Research Scientist
Recommender Systems.(Source: d2l.ai/index.html).

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