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Beginning Application Development with TensorFlow and Keras: Learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applications

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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

  1. Introduction to Neural Networks and Deep Learning
  2. Model Architecture
  3. Model Evaluation and Optimization
  4. Productization
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