Exploring Deep Learning
With chapters selected by Andrew Trask
  • January 2020
  • ISBN 9781617297816
  • 67 pages
Deep learning has already made incredible progress in many areas—including natural language processing, image recognition, and identifying complex patterns in data—giving rise to virtual personal assistants, interactive chatbots, self-driving cars, and improvements in medical diagnostics. With continued advances in AI, increasing availability of data, and faster, more powerful computers, deep learning promises to provide countless opportunities for new and exciting future innovations.

About the book

Exploring Deep Learning combines three chapters from Manning books, selected by author and experienced deep learning practitioner Andrew Trask. In it, you’ll get a high-level view of basic deep learning concepts and take a look at different learning techniques, including supervised vs. unsupervised learning and parametric vs. non-parametric learning. Using Tensorflow, you’ll also explore more advanced concepts, such as classification, recurrent neural networks (RNNs), seq2seq architecture, vector representation, and embedding natural language as you build a working chatbot. With this timely and accessible sampler, you’ll have a firm foundation for building on your deep learning education as you discover for yourself deep learning’s potential for the future.
Table of Contents detailed table of contents


What is deep learning?

What is deep learning?

1.1 Artificial intelligence, machine learning, and deep learning

1.2 Before deep learning: a brief history of machine learning

1.3 Why deep learning? Why now?

Fundamental concepts: how do machines learn?

Fundamental concepts: how do machines learn?

2.1 What is deep learning?

2.2 What is machine learning?

2.3 Supervised machine learning

2.4 Unsupervised machine learning

2.5 Parametric vs. nonparametric learning

2.6 Supervised parametric learning

2.7 Unsupervised parametric learning

2.8 Nonparametric learning


Sequence-to-sequence models for chatbots

Sequence-to-sequence models for chatbots

11.1 Building on classification and RNNs

11.2 Seq2seq architecture

11.3 Vector representation of symbols

11.4 Putting it all together

11.5 Gathering dialogue data

11.6 Summary

What's inside

  • “What is Deep Learning?” - Chapter 1 from Deep Learning with Python by François Chollet
  • “Fundamental concepts: how do machines learn?” - Chapter 2 from Grokking Deep Learning by Andrew Trask
  • “Sequence-to-sequence models for chatbots” – Chapter 11 from Machine Learning with TensorFlow by Nishant Shukla with Kenneth Fricklas

About the author

Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world’s largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform.

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