Exploring Deep Learning

by Andrew W. Trask (Editor, Contributor), François Chollet (Contributor), Nishant Shukla (Contributor)

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.

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.

67 pages, ebook

Published January 1, 2020