Beginning Application Development with TensorFlow and Keras (PDF)

ISBN: 1789537290

Language: English

Category: Uncategorized


Posted on 2019-04-21, by manhneovn.

Description

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Packt | English | 2018 | ISBN-10: 1789537290 | 148 pages | PDF | 7.43 MB

by Luis Capelo (Author)

You need much more than imagination to predict earthquakes and detect brain cancer cells. Become an expert in designing and deploying TensorFlow and Keras models, and generate insightful predictions with the power of deep learning.

Key Features
Cover the basics of neural networks and choose the right model architecture
Make predictions with a trained model and get to grips with TensorBoard
Evaluate metrics and techniques and deploy a model as a web application
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 book, you'll build a Bitcoin application that predicts the future price, based on historic, and freely available information.

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 book 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 book.

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

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