TensorFlow Machine Learning Cookbook: Over 60 recipes to build intelligent machine learning systems with the power of Python, 2nd Edition

TensorFlow Machine Learning Cookbook: Over 60 recipes to build intelligent machine learning systems with the power of Python, 2nd Edition
TensorFlow Machine Learning Cookbook: Over 60 recipes to build intelligent machine learning systems with the power of Python, 2nd Edition by Nick McClure
English | 2018 | ISBN: 1789131680 | 422 Pages | True PDF, EPUB | 22 MB

TensorFlow Machine Learning Cookbook: Over 60 recipes to build intelligent machine learning systems with the power of Python, 2nd Edition
Skip the theory and get the most out of Tensorflow to build production-ready machine learning 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.
What You Will Learn

  • 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