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

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

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

學習設計、開發、訓練和部署 TensorFlow 和 Keras 模型作為真實世界應用程式

主要特點:
- 專注於神經網絡及其基本操作
- 準備數據以供深度學習模型使用，並使用 Flask 和 HTTP API 將其部署為互動式網絡應用程式
- 使用 Keras，一個 TensorFlow 抽象庫

書籍描述:
通過本書，您將學習如何將 TensorFlow 和 Keras 模型訓練、評估和部署為真實世界的網絡應用程式。在實踐介紹之後，您將使用一個示例模型來探索深度學習的細節，選擇能夠解決特定問題的正確層。在課程結束時，您將構建一個基於歷史和免費可用信息的比特幣應用程式，用於預測未來價格。

本書還將為您提供構建使用深度學習模型生成預測的應用程式的藍圖。從那裡，您可以繼續改進我們的示例模型-通過添加更多數據、計算更多特徵或更改其架構-不斷提高其預測準確性，或者根據需要創建全新的模型，改變應用程式的核心組件。

您將學到什麼:
- 設置深度學習編程環境
- 探索神經網絡的常見組件及其基本操作
- 準備深度學習模型所需的數據
- 使用 Flask 和 HTTP API 將模型部署為互動式網絡應用程式
- 使用 Keras，一個 TensorFlow 抽象庫
- 探索神經網絡解決的問題類型

本書適合對象:
本課程適合有經驗的開發人員、分析師或數據科學家，他們希望使用 TensorFlow 和 Keras 開發應用程式。這個快速實踐課程將迅速向您展示如何在真實應用程式開發的背景下掌握 TensorFlow。我們假設您熟悉 Python，並具有基本的網絡應用程式開發知識。如果您具備線性代數、概率和統計的背景，您將輕鬆理解課程中討論的概念。

目錄:
1. 神經網絡和深度學習簡介
2. 模型架構
3. 模型評估和優化
4. 產品化