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You're reading from Python Deep Learning Cookbook Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python

Product type Paperback

Published in Oct 2017

Publisher Packt

ISBN-13 9781787125193

Length 330 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Author (1):

Indra den Bakker

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Table of Contents (15) Chapters

Preface

1. Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks

2. Feed-Forward Neural Networks FREE CHAPTER

3. Convolutional Neural Networks

4. Recurrent Neural Networks

5. Reinforcement Learning

6. Generative Adversarial Networks

7. Computer Vision

8. Natural Language Processing

9. Speech Recognition and Video Analysis

10. Time Series and Structured Data

11. Game Playing Agents and Robotics

12. Hyperparameter Selection, Tuning, and Neural Network Learning

13. Network Internals

14. Pretrained Models

Pretrained Models

This chapter provides a collection of recipes based on popular deep learning models such as VGG-16 and Inception V4:

Large-scale visual recognition with GoogLeNet/Inception
Extracting bottleneck features with ResNet
Leveraging pretrained VGG models for new classes
Fine-tuning with Xception

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Authors (1)

den Bakker

Indra den Bakker is an experienced deep learning engineer and mentor. He is the founder of 23insightspart of NVIDIA's Inception programa machine learning start-up building solutions that transform the worlds most important industries. For Udacity, he mentors students pursuing a Nanodegree in deep learning and related fields, and he is also responsible for reviewing student projects. Indra has a background in computational intelligence and worked for several years as a data scientist for IPG Mediabrands and Screen6 before founding 23insights.

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