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Deep Learning Quick Reference

You're reading from   Deep Learning Quick Reference Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788837996
Length 272 pages
Edition 1st Edition
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Author (1):
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Mike Bernico Mike Bernico
Author Profile Icon Mike Bernico
Mike Bernico
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Table of Contents (15) Chapters Close

Preface 1. The Building Blocks of Deep Learning FREE CHAPTER 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

Who this book is for

I'm a practicing data scientist, and I'm writing this book keeping other practicing data scientists and machine learning engineers in mind. If you're a software engineer applying deep learning, this book is also for you.

If you're a deep learning researcher, then this book isn't really for you; however, you should still pick up a copy so that you can criticize the lack of proofs and mathematical rigor in this book.

If you're an academic or educator, then this book is definitely for you. I've taught a survey source in data science at the University of Illinois at Springfield (go Prairie Stars!) for the past 3 years, and in doing so, I've had the opportunity to inspire a number of future machine learning people. This experience has inspired me to create this book. I think a book like this is a great way to help students build interest in a very complex topic.

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