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Deep Learning Quick Reference

You're reading from  Deep Learning Quick Reference

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781788837996
Pages 272 pages
Edition 1st Edition
Languages
Author (1):
Mike Bernico Mike Bernico
Profile icon Mike Bernico

Table of Contents (15) Chapters

Preface 1. The Building Blocks of Deep Learning 2. Using Deep Learning to Solve Regression Problems 3. Monitoring Network Training Using TensorBoard 4. Using Deep Learning to Solve Binary Classification Problems 5. Using Keras to Solve Multiclass Classification Problems 6. Hyperparameter Optimization 7. Training a CNN from Scratch 8. Transfer Learning with Pretrained CNNs 9. Training an RNN from scratch 10. Training LSTMs with Word Embeddings from Scratch 11. Training Seq2Seq Models 12. Using Deep Reinforcement Learning 13. Generative Adversarial Networks 14. Other Books You May Enjoy

An introduction to natural language processing

The field of NLP is vast and complex. Any interaction between human language and computer science might technically fall into this category. For the sake of this discussion though, I'll confine NLP to analyzing, understanding, and, sometimes, generating human language.

From the beginnings of computer science, we've been fascinated by NLP as a gateway to strong artificial intelligence. In 1950, Alan Turing proposed the Turing test, which involves a computer impersonating a human so well that it's indistinguishable from another human, as a metric for machine intelligence. Ever since, we've worked to find clever ways to help machines understand human language. Along the way, we've developed speech-to-text transcription, automatic translation between human languages, the automatic summation of documents, topic...

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