Book Image

The Applied TensorFlow and Keras Workshop

By : Harveen Singh Chadha, Luis Capelo
Book Image

The Applied TensorFlow and Keras Workshop

By: Harveen Singh Chadha, Luis Capelo

Overview of this book

Machine learning gives computers the ability to learn like humans. It is becoming increasingly transformational to businesses in many forms, and a key skill to learn to prepare for the future digital economy. As a beginner, you’ll unlock a world of opportunities by learning the techniques you need to contribute to the domains of machine learning, deep learning, and modern data analysis using the latest cutting-edge tools. The Applied TensorFlow and Keras Workshop begins by showing you how neural networks work. After you’ve understood the basics, you will train a few networks by altering their hyperparameters. To build on your skills, you’ll learn how to select the most appropriate model to solve the problem in hand. While tackling advanced concepts, you’ll discover how to assemble a deep learning system by bringing together all the essential elements necessary for building a basic deep learning system - data, model, and prediction. Finally, you’ll explore ways to evaluate the performance of your model, and improve it using techniques such as model evaluation and hyperparameter optimization. By the end of this book, you'll have learned how to build a Bitcoin app that predicts future prices, and be able to build your own models for other projects.
Table of Contents (6 chapters)

Introduction

This chapter focuses on how to productize a deep learning model. We use the word productize to define the creation of a software product from a deep learning model that can be used by other people and applications.

We are interested in models that use new data as and when it becomes available, continuously learn patterns from new data, and consequently, make better predictions. In this chapter, we will study two strategies to deal with new data: one that retrains an existing model, and another that creates a completely new model. Then, we implement the latter strategy in our Bitcoin price prediction model so that it can continuously predict new Bitcoin prices.

By the end of this chapter, we will be able to deploy a working web application (with a functioning HTTP API) and modify it to our heart's content.