Keras 2.x Projects: 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras
暫譯: Keras 2.x 專案：9 個專案展示使用 Keras 進行神經網路和深度學習應用的快速實驗

Demonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.x

Key Features

• Experimental projects showcasing the implementation of high-performance deep learning models with Keras.
• Use-cases across reinforcement learning, natural language processing, GANs and computer vision.
• Build strong fundamentals of Keras in the area of deep learning and artificial intelligence.

Book Description

Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas.

To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more.

By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.

What you will learn

• Apply regression methods to your data and understand how the regression algorithm works
• Understand the basic concepts of classification methods and how to implement them in the Keras environment
• Import and organize data for neural network classification analysis
• Learn about the role of rectified linear units in the Keras network architecture
• Implement a recurrent neural network to classify the sentiment of sentences from movie reviews
• Set the embedding layer and the tensor sizes of a network

Who this book is for

If you are a data scientist, machine learning engineer, deep learning practitioner or an AI engineer who wants to build speedy intelligent applications with minimal lines of codes, then this book is the best fit for you. Sound knowledge of machine learning and basic familiarity with Keras library would be useful.

Table of Contents

1. Getting Started With Keras
2. Modeling Real Estate Market Using Regression Analysis
3. Heart Disease Classification With A Neural Network
4. Concrete Quality Prediction Using Deep Neural Network
5. Fashion Articles Recognition By A Convolutional Neural Network
6. Movie Reviews Sentiment Analysis Using Recurrent Neural Network
7. Stock Volatility Forecasting Using Long Short-Term Memory
8. Reconstruction Of Handwritten Digit Images Using Autoencoder
9. Robot control system using Deep Reinforcement Learning
10. Reuters newswire topics classifier in Keras
11. What is next?