Beginning Application Development with TensorFlow and Keras

Learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applications

Key Features
Focus on neural network and its essential operations
Prepare data for a deep learning model and deploy it as an interactive web application, with Flask and a HTTP API
Use Keras, a TensorFlow abstraction library
Book Description
With this book, you'll learn how to train, evaluate and deploy Tensorflow and Keras models as real-world web applications. After a hands-on introduction, you'll use a sample model to explore the details of deep learning, selecting the right layers that can solve a given problem. By the end of the course, you'll build a Bitcoin application that predicts the future price, based on historic, and freely available information.

This book will also provide you with a blueprint for how to build an application that generates predictions using a deep learning model. From there, you can continue to improve our example model― either by adding more data, computing more features, or changing its architecture―continuously increasing its prediction accuracy, or create a completely new model, changing the core components of the application as you see fit.

What you will learn
Set up a deep learning programming environment
Explore the common components of a neural network and its essential operations
Prepare data for a deep learning model
Deploy model as an interactive web application, with Flask and a HTTP API
Use Keras, a TensorFlow abstraction library
Explore the types of problems addressed by neural networks
Who This Book Is For
This course is ideal for experienced developers, analysts, or a data scientists, who want to develop applications using TensorFlow and Keras. This rapid hands-on course quickly shows you how to get to grips with TensorFlow in the context of real-world application development. We assume that you are familiar with Python and have a basic knowledge of web application development. If you have a background in linear algebra, probability, and statistics, you will easily grasp concepts that are discussed in the course.

Table of Contents
Introduction to Neural Networks and Deep Learning
Model Architecture
Model Evaluation and Optimization
Productization