Mastering TensorFlow 1.x
We cover advanced deep learning concepts (such as transfer learning, generative adversarial models, and reinforcement learning), and implement them using TensorFlow and Keras. We cover how to build and deploy at scale with distributed models. You will learn to build TensorFlow models using R, Keras, TensorFlow Learn, TensorFlow Slim and Sonnet
Offered by
Difficulty Level
Intermediate
Completion Time
15h48m
Language
English
About Book
Who Is This Book 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.
Mastering TensorFlow 1.x
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 15h48m total length
Tensorflow 101
High-Level Libraries for TensorFlow
Keras 101
Classical Machine Learning with TensorFlow
Neural Networks and MLP with TensorFlow and Keras
RNN with TensorFlow and Keras
RNN for Time Series Data with TensorFlow and Keras
RNN for Text Data with TensorFlow and Keras
CNN with TensorFlow and Keras
Autoencoder with TensorFlow and Keras
TensorFlow Models in Production with TF Serving
Transfer Learning and Pre-Trained Models
Deep Reinforcement Learning
Generative Adversarial Networks
Distributed Models with TensorFlow Clusters
TensorFlow Models on Mobile and Embedded Platforms
TensorFlow and Keras in R
Debugging TensorFlow Models
TensorFlow Processing Units
Related Resources
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