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Keras 2.x Projects
Keras 2.x Projects

Keras 2.x Projects: 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras

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Profile Icon Giuseppe Ciaburro
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Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (2 Ratings)
Paperback Dec 2018 394 pages 1st Edition
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Arrow left icon
Profile Icon Giuseppe Ciaburro
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€18.99 per month
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (2 Ratings)
Paperback Dec 2018 394 pages 1st Edition
eBook
€20.98 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€20.98 €29.99
Paperback
€36.99
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Free Trial
Renews at €18.99p/m

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Keras 2.x Projects

Modeling Real Estate Using Regression Analysis

The real estate market is a type of market where the sales and purchases between sellers and buyers refer to the exchange of real estate of any kind, such as housing, land, commercial premises, and so on. Real estate prices depend on a series of factors that make the asset more palatable for potential buyers. Regression analysis is the statistical process of studying the relationship between a set of independent variables (explanatory variables) and the dependent variable (response variable). Through this technique, it is possible to understand how the value of the response variable changes when the explanatory variable is varied. In this chapter, the real estate market will be modeled through a regression analysis.

In this chapter, we will cover the following topics:

  • Defining a regression problem
  • Creating a linear regression model...

Defining a regression problem

Regression analysis is the starting point in data science. This is because regression models represent the most well-understood models in numerical simulation. Once we experience the workings of regression models, we will be able to understand all other machine learning algorithms. Regression models are easily interpretable as they are based on solid mathematical bases (such as matrix algebra, for example). In the following sections, we will see that linear regression allows us to derive a mathematical formula that's representative of the corresponding model. Perhaps this is why such techniques are extremely easy to understand.

Regression analysis is a statistical process that's implemented to study the relationship between a set of independent variables (explanatory variables) and the dependent variable (response variable). Through this...

Creating a linear regression model

A simple linear regression is easy to understand, but represents the basis of regression techniques. Once these concepts are understood, it will be easier for us to address the other types of regression. To begin with, let's take an example of applying linear regression that's been taken from the real world.

Consider some data that has been collected on a group of bikers, which consists of the following aspects:

  • Number of years of use
  • Number of kilometers traveled in one year
  • Number of falls

Through these techniques, we find that, on average, when the number of kilometers traveled increases, the number of falls also increases. By increasing the number of years of motorcycle usage and by increasing the experience, the number of falls tends to decrease.

The linear regression method consists of precisely identifying a line that is capable...

Multiple linear regression concepts

So far, we have resolved simple linear regression problems that study the relation between a dependent variable, y, and an independent variable, x, based on the following regression equation:

In this equation, the explanatory variable is represented by x and the response variable is represented by y. To solve this problem, the least squares method was used. In this method, we can find the best fit by minimizing the sum of squares of the vertical distances from each data point on the line. As mentioned previously, we don't find that a variable depends solely on another very often. Usually, we find that the response variable depends on at least two predictors. In practice, we will have to create models with a response variable that depend on more than one predictor. These models are...

Neural networks for regression using Keras

The real estate market is a market where the sales and purchase between sellers and buyers refer to the exchange of real estate of any kind, such as housing, land, commercial premises, and so on. Real estate prices depend on a series of factors that make the asset more palatable for potential buyers.

These factors include the socioeconomic conditions, environmental conditions, and educational facilities of the area in which the property is located. Analyzing how these factors affect the cost of real estate can be a valuable tool for technicians in the sector in order to predict the market trends, depending on the changes that are occurring.

To do this, we will run a neural network regression for the Boston dataset; the median values of owner-occupied homes are predicted for the test data. The dataset describes 13 numerical properties...

Summary

In this chapter, you've learned about the different types of regression techniques. Regression analysis is a statistical process that's done to study the relationship between a set of independent variables (explanatory variables) and a dependent variable (response variable). Regression algorithms show you how the value of the response variable changes when the explanatory variable is varied. The concepts underlying regression were explored. Furthermore, we gained an understanding of the differences between simple and multiple regression. Later, we saw how a simple and multiple linear regression problem is represented algebraically. Thus, we have analyzed how a regression problem is solved through the least squares algorithm.

The second part of this chapter was dedicated to the practical resolution of a multiple regression problem using the keras library. Modeling...

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Key benefits

  • 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.

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.

Who is this book 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.

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

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Publication date : Dec 31, 2018
Length: 394 pages
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Language : English
ISBN-13 : 9781789536645
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Product Details

Publication date : Dec 31, 2018
Length: 394 pages
Edition : 1st
Language : English
ISBN-13 : 9781789536645
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Languages :
Concepts :
Tools :

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Table of Contents

12 Chapters
Getting Started with Keras Chevron down icon Chevron up icon
Modeling Real Estate Using Regression Analysis Chevron down icon Chevron up icon
Heart Disease Classification with Neural Networks Chevron down icon Chevron up icon
Concrete Quality Prediction Using Deep Neural Networks Chevron down icon Chevron up icon
Fashion Article Recognition Using Convolutional Neural Networks Chevron down icon Chevron up icon
Movie Reviews Sentiment Analysis Using Recurrent Neural Networks Chevron down icon Chevron up icon
Stock Volatility Forecasting Using Long Short-Term Memory Chevron down icon Chevron up icon
Reconstruction of Handwritten Digit Images Using Autoencoders Chevron down icon Chevron up icon
Robot Control System Using Deep Reinforcement Learning Chevron down icon Chevron up icon
Reuters Newswire Topics Classifier in Keras Chevron down icon Chevron up icon
What is Next? Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(2 Ratings)
5 star 50%
4 star 0%
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1 star 50%
Nick Nikolov Mar 19, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I want to emphasize first that this is not a book for understanding the concepts deeply. You shouldn't just show up to a data science position and say, "All I know about LSTM layers in recurrent networks is that they have memory like traditional RNN's but they also have gates."However, if you have a mathematical resource that explains the algorithms and model framework, then you will benefit greatly from this book. It's incredibly good at explaining what it is you are doing for each project.
Amazon Verified review Amazon
y. b. Apr 19, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
This book attracts by some interesting examples. But it is poorly written. Conceptual part has no depth and often is unrelated to the project, really. Code is buggy and does not show that the author even understand what he is doing in the code. For example, in thefashion-mnist project input shape is screwed up and the chapter shows results of 1000 epochs with this network; in chapter on RNN network uses metric 'acc' for prediction of real-valued label.
Amazon Verified review Amazon
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