Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

Build, scale, and deploy deep neural network models using the star libraries in Python

Key Features

• 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

Book Description

TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs.

This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images.

You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected.

This book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems.

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 tasks
• Build end-to-end deep learning (CNN, RNN, and Autoencoders) models with TensorFlow
• Scale and deploy production models with distributed and high-performance computing on GPU and clusters
• Build TensorFlow models to work with multilayer perceptrons using Keras, TFLearn, and R
• Learn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devices
• Supercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow Clusters

Table of Contents

1. Tensorflow 101
2. High Level Libraries for TensorFlow
3. Keras 101
4. Classical Machine Learning with TensorFlow
5. Neural Networks and MLP with TensorFlow and Keras
6. RNN with TensorFlow and Keras
7. RNN for Time Series Data with TensorFlow and Keras
8. NLP for Text Data with TensorFlow and Keras
9. CNN with TensorFlow and Keras
10. Autoencoder with TensorFlow and Keras
11. TensorFlow Models in Production with TF Serving
12. Transfer Learning and Pre-Trained Models
13. Deep Reinforcement Learning
14. Generative Adversarial Networks
15. Distributed Models with TensorFlow Clusters
16. TensorFlow on Mobile and Embedded Platforms
17. TensorFlow and Keras in R
18. Debugging TensorFlow Models
19. Appendix A: TPU

Armando Fandango is an accomplished technologist with hands-on capabilities and senior executive level experience with startups and large companies globally. Armando is spearheading Epic Engineering and Consulting Group as Chief Data Scientist. His work spans across diverse industries including FinTech, Banking, BioInformatics, Genomics, AdTech, Utilities and Infrastructure, Traffic and Transportation, Energy, Human Resource, and Entertainment.

Armando has worked for more than ten years in projects involving Predictive Analytics, Data Science, Machine Learning, Big Data, Product Engineering and High-Performance Computing. His research interests span across machine learning, deep learning, algorithmic game theory and scientific computing. Armando has authored book titled "Python Data Analysis - Second Edition" and published research in international journals and conferences.