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 Tensor Flow on mobile, and distributed Tensor Flow on GPU, Clusters, and Kubernetes
Book Description
Tensor Flow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. Tensor Flow represents the data as tensors and the computation as graphs.
This book is a comprehensive guide that lets you explore the advanced features of Tensor Flow 1.x. Gain insight into Tensor Flow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of Tensor Flow 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 Tensor Flow1.x, such as distributed Tensor Flow with TF Clusters, deploy production models with Tensor Flow Serving, and build and deploy Tensor Flow models for mobile and embedded devices on Android and i OS platforms. You will see how to call Tensor Flow and Keras API within the R statistical software, and learn the required techniques for debugging when the Tensor Flow API-based code does not work as expected.
The book helps you obtain in-depth knowledge of Tensor Flow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of Tensor Flow 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 Tensor Flow 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 Tensor Flow
- Scale and deploy production models with distributed and high-performance computing on GPU and clusters
- Build Tensor Flow models to work with multilayer perceptrons using Keras, TFLearn, and R
- Learn the functionalities of smart apps by building and deploying Tensor Flow models on i OS and Android devices
- Supercharge Tensor Flow with distributed training and deployment on Kubernetes and Tensor Flow Clusters
Who this book is for
This book is for data scientists, machine learning engineers, artificial intelligence engineers, and for all Tensor Flow users who wish to upgrade their Tensor Flow 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 Tensor Flow is required to get the most out of the book.
Armando Fandango creates AI-empowered products by leveraging his expertise in deep learning, computational methods, and distributed computing. He advises Owen.ai Inc on AI product strategy. He founded Neura Sights Inc. with the goal of creating insights using neural networks. He is the founder of Vets2Data Inc., a non-profit organization assisting US military veterans in building AI skills. Armando has authored books titled Python Data Analysis – 2nd Edition and Mastering Tensor Flow and published research in international journals and conferences.
