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Can I read Mastering TensorFlow 1.x : Advanced Machine Learning and Deep Learning Concepts Using TensorFlow 1.x and Keras on EtoBox?

Mastering TensorFlow 1.x : Advanced Machine Learning and Deep Learning Concepts Using TensorFlow 1.x and Keras by Fandango, Armando is a nonfiction available to read on EtoBox.

What is Mastering TensorFlow 1.x : Advanced Machine Learning and Deep Learning Concepts Using TensorFlow 1.x and Keras about?

**Build, scale, and deploy deep neural network models using the star libraries in Python** **About This Book** * Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras * Build, deploy, and scale end-to-end deep neural network models in a production environment * Learn to deploy TensorFlow on mobile, and distributed TensorFlow on GPU, Clusters, and Kubernetes **Who This Book Is For** This book is for data scientists, machine learning engineers, artificial intelligence engineers, and for all TensorFlow users who wish to upgrade their TensorFlow knowledge and work on various machine learning and deep learning problems. If you are looking for an easy-to-follow guide that underlines the intricacies and complex use cases of machine learning, you will find this book extremely useful. Some basic understanding of TensorFlow is required to get the most out of the book. **What You Will Learn** * Master advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and more, using TensorFlow and Keras * Perform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning

Who reads Mastering TensorFlow 1.x : Advanced Machine Learning and Deep Learning Concepts Using TensorFlow 1.x and Keras?

It is typically read by self-directed learners exploring a subject in depth.

Common subject areas: history, science, philosophy, social sciences.

Author
Fandango, Armando
Publisher
Packt Publishing - ebooks Account
Published
2018
Language
EN
ISBN
9781788297004
Category
nonfiction
Subjects
Computer Science, Science, Stem

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