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

Skip the theory and get the most out of Tensorflow to build production-ready machine learning models
Key Features

  • 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

    Book Description 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.
    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

    Who this book is for If you are a data scientist or a machine learning engineer with some knowledge of linear algebra, statistics, and machine learning, this book is for you. If you want to skip the theory and build production-ready machine learning models using Tensorflow without reading pages and pages of material, this book is for you. Some background in Python programming is assumed.

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