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Deep Learning Essentials

You're reading from   Deep Learning Essentials Your hands-on guide to the fundamentals of deep learning and neural network modeling

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781785880360
Length 284 pages
Edition 1st Edition
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Authors (3):
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Wei Di Wei Di
Author Profile Icon Wei Di
Wei Di
Anurag Bhardwaj Anurag Bhardwaj
Author Profile Icon Anurag Bhardwaj
Anurag Bhardwaj
Jianing Wei Jianing Wei
Author Profile Icon Jianing Wei
Jianing Wei
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Toc

Table of Contents (12) Chapters Close

Preface 1. Why Deep Learning? FREE CHAPTER 2. Getting Yourself Ready for Deep Learning 3. Getting Started with Neural Networks 4. Deep Learning in Computer Vision 5. NLP - Vector Representation 6. Advanced Natural Language Processing 7. Multimodality 8. Deep Reinforcement Learning 9. Deep Learning Hacks 10. Deep Learning Trends 11. Other Books You May Enjoy

Preface

Deep learning is the most disruptive trend in the tech world, having jumped out of research laboratories right into production environments. It is the science and art of working through hidden layers of data to get deep insights. Deep learning is currently one of the best providers of solutions to problems in image recognition, speech recognition, object recognition, and natural language processing (NLP).

We’ll start off by brushing up on machine learning and quickly get into the fundamentals of deep learning and its implementation. Moving on, we’ll teach you about the different types of neural networks and their applications in the real world. With the help of insightful examples, you’ll learn to recognize patterns using a deep neural network and get to know other important concepts such as data manipulation and classification.

Using the reinforcement learning technique with deep learning, you’ll build AI that can outperform any human and also work with the LSTM network. During the course of this book, you will come across a wide range of different frameworks and libraries, such as TensorFlow, Python, Nvidia, and others. By the end of the book, you’ll be able to deploy a production-ready deep learning framework for your own applications.

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