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- Exploit the features of Tensorflow to build and deploy machine learning models
- Train neural networks to tackle real-world problems in Computer Vision and NLP
- Handy techniques to write production-ready code for your Tensorflow models

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before.
With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production.
By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.

Become familiar with the basic features of the TensorFlow library
Get to know Linear Regression techniques with TensorFlow
Learn SVMs with hands-on recipes
Implement neural networks to improve predictive modeling
Apply NLP and sentiment analysis to your data
Master CNN and RNN through practical recipes
Implement the gradient boosted random forest to predict housing prices
Take TensorFlow into production

Download this book in **EPUB** and **PDF** formats

Access this title in our online reader with advanced features

Publication date :
Aug 31, 2018

Length
422 pages

Edition :
2nd Edition

Language :
English

ISBN-13 :
9781789131680

Category :

Concepts :

Tools :

Preface

1. Getting Started with TensorFlow

2. The TensorFlow Way

3. Linear Regression

4. Support Vector Machines

5. Nearest-Neighbor Methods

6. Neural Networks

7. Natural Language Processing

8. Convolutional Neural Networks

9. Recurrent Neural Networks

10. Taking TensorFlow to Production

11. More with TensorFlow

12. Other Books You May Enjoy

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