Mastering TensorFlow 2.x

Key Features
● Combines theory and implementation with in-detail use-cases.
● Coverage on both, TensorFlow 1.x and 2.x with elaborated concepts.
● Exposure to Distributed Training, GANs and Reinforcement Learning.
Description
Mastering TensorFlow 2.x is a must to read and practice if you are interested in building various kinds of neural networks with high level TensorFlow and Keras APIs. The book begins with the basics of TensorFlow and neural network concepts, and goes into specific topics like image classification, object detection, time series forecasting and Generative Adversarial Networks.
While we are practicing TensorFlow 2.6 in this book, the version of Tensorflow will change with time; however you can still use this book to witness how Tensorflow outperforms. This book includes the use of a local Jupyter notebook and the use of Google Colab in various use cases including GAN and Image classification tasks. While you explore the performance of TensorFlow, the book also covers various concepts and in-detail explanations around reinforcement learning, model optimization and time series models.
What you will learn
● Getting started with Tensorflow 2.x and basic building blocks.
● Get well versed in functional programming with TensorFlow.
● Practice Time Series analysis along with strong understanding of concepts.
● Get introduced to use of TensorFlow in Reinforcement learning and Generative Adversarial Networks.
● Train distributed models and how to optimize them.
Who this book is for
This book is designed for machine learning engineers, NLP engineers and deep learning practitioners who want to utilize the performance of TensorFlow in their ML and AI projects. Readers are expected to have some familiarity with Tensorflow and the basics of machine learning would be helpful.
Table of Contents
1. Getting started with TensorFlow 2.x
2. Machine Learning with TensorFlow 2.x
3. Keras based APIs
4. Convolutional Neural Networks in Tensorflow
5. Text Processing with TensorFlow 2.x
6. Time Series Forecasting with TensorFlow 2.x
7. Distributed Training and DataInput pipelines
8. Reinforcement Learning
9. Model Optimization
10. Generative Adversarial Networks


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