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R Deep Learning Projects
R Deep Learning Projects

R Deep Learning Projects: Master the techniques to design and develop neural network models in R

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R Deep Learning Projects

Traffic Sign Recognition for Intelligent Vehicles

Convolutional neural networks (CNNs) are so useful in computer vision that we are going to use one for another application, traffic sign detection for intelligent vehicles. We will also cover several important concepts of deep learning in this chapter and will get readers exposed to other popular frameworks and libraries for deep learning.

We continue our R deep learning journey with one of the core problems in self-driving cars, object recognition, and to be specific, traffic sign classification. To avoid accidents and ensure safety, robust traffic sign classification is critical to realizing driving autonomy. We will start with what self-driving cars are and what aspects deep learning is applied to. We will also discuss how deep learning stands out and becomes the state-of-the-art solution for object recognition in intelligent...

How is deep learning applied in self-driving cars?

A self-driving car (also called an autonomous/automated vehicle or driverless car) is a robotic vehicle that is capable of traveling between destinations and navigating without human intervention. To enable autonomy, self-driving cars detect and interpret environments using a variety of techniques such as radar, GPS and computer vision; and they then plan appropriate navigational paths to the desired destination.

In more detail, the following is how self-driving cars work in general:

  • The software plans the routes based on the destination, traffic, and road information and starts the car
  • A Light Detection and Ranging (LiDAR) sensor captures the surroundings in real time and creates a dynamic 3D map
  • Sensors monitor lateral movement to calculate the car's position on the 3D map
  • Radar systems exploit information on distances...

Traffic sign recognition using CNN

Getting started with exploring GTSRB

The GTSRB dataset, compiled and generously published by the real-time computer vision research group in Institut für Neuroinformatik, was originally used for a competition of classifying single images of traffic signs. It consists of a training set of 39,209 labeled images and a testing test of 12,630 unlabeled images. The training dataset contains 43 classes—43 types of traffic signs. We will go through all classes and exhibit several samples for each class.

The dataset can...

Dealing with a small training set – data augmentation

We have been very fortunate so far to possess a large-enough training dataset with 75% of 39,209 samples. This is one of the reasons why we are able to achieve a 99.3% to 99.4% classification accuracy. However, in reality, obtaining a large training set is not easy in most supervised learning cases, where manual work is necessary or the cost of data collection and labeling is high. In our traffic signs classification project, can we still achieve the same performance if we are given a lot less training samples to begin with? Let's give it a shot.

We simulate a small training set with only 10% of the 39,209 samples and a testing set with the rest 90%:

> train_perc_1 = 0.1 
> train_index_1 <- createDataPartition(data.y, p=train_perc_1, list=FALSE) 
> train_index_1 <- train_index_1[sample(nrow(train_index_1...

Reviewing methods to prevent overfitting in CNNs

Overfitting occurs when the model fits too well to the training set but is not able to generalize to unseen cases. For example, a CNN model recognizes specific traffic sign images in the training set instead of general patterns. It can be very dangerous if a self-driving car is not able to recognize sign images in ever-changing conditions, such as different weather, lighting, and angles different from what are presented in the training set. To recap, here's what we can do to reduce overfitting:

  • Collecting more training data (if possible and feasible) in order to account for various input data.
  • Using data augmentation, wherein we invent data in a smart way if time or cost does not allow us to collect more data.
  • Employing dropout, which diminishes complex co-adaptations among neighboring neurons.
  • Adding Lasso (L1) or/and...

Summary

We just accomplished our second computer vision project in this R and deep learning journey! Through this chapter, we got more familiar with convolutional neural networks and their implementation in MXNet, and another powerful deep learning tool: Keras with TensorFlow.

We started with what self-driving cars are and how deep learning techniques are making self-driving cars feasible and more reliable. We also discussed how deep learning stands out and becomes the state-of-the-art solution for object recognition in intelligent vehicles. After exploring the traffic sign dataset, we developed our first CNN model using MXNet and achieved more than 99% accuracy. Then we moved on to another powerful deep learning framework, Keras + TensorFlow, and obtained comparable results.

We introduced the dropout technique to reduce overfitting. We also learned how to deal with lack of training...

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Key benefits

  • Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more
  • Get to grips with R's impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec
  • Practical projects that show you how to implement different neural networks with helpful tips, tricks, and best practices

Description

R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting.

Who is this book for?

Machine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book a useful resource. A knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book.

What you will learn

  • Instrument Deep Learning models with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec
  • Apply neural networks to perform handwritten digit recognition using MXNet
  • Get the knack of CNN models, Neural Network API, Keras, and TensorFlow for traffic sign classification -Implement credit card fraud detection with Autoencoders
  • Master reconstructing images using variational autoencoders
  • Wade through sentiment analysis from movie reviews
  • Run from past to future and vice versa with bidirectional Long Short-Term Memory (LSTM) networks
  • Understand the applications of Autoencoder Neural Networks in clustering and dimensionality reduction

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Publication date : Feb 22, 2018
Length: 258 pages
Edition : 1st
Language : English
ISBN-13 : 9781788478403
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Product Details

Publication date : Feb 22, 2018
Length: 258 pages
Edition : 1st
Language : English
ISBN-13 : 9781788478403
Vendor :
Google
Category :
Languages :
Concepts :
Tools :

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Table of Contents

6 Chapters
Handwritten Digit Recognition Using Convolutional Neural Networks Chevron down icon Chevron up icon
Traffic Sign Recognition for Intelligent Vehicles Chevron down icon Chevron up icon
Fraud Detection with Autoencoders Chevron down icon Chevron up icon
Text Generation Using Recurrent Neural Networks Chevron down icon Chevron up icon
Sentiment Analysis with Word Embeddings 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 Full star icon Empty star icon 4
(5 Ratings)
5 star 60%
4 star 20%
3 star 0%
2 star 0%
1 star 20%
A. Albert Mar 18, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
There are many resources for Python Deep Learning but as a R user, this book is only one that I found coming with practical examples. Just finished chapter one, but so far it is a good read.
Amazon Verified review Amazon
Andrii Apr 25, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
R Deep Learning Projects is a detailed guide to design and develop a deep neural networkmodels in R. There are many of R-language users, which have a lot of experience with this popular language for statistical analysis, signal processing, and machine learning.This book for users who want to use deep learning abilities in their projects but do not know how to integrate it with R-ecosystem. Readers with a strong math background and some experience with R-language will find everything to start their own R deep learning projects.This book should not be your first book about R or neural networks. You’ll start with the overview of neural networks and deep learning and implement handwritten digit recognition using CNN. A lot of code examples helps you to create your own project related to convolution networks usage. Then you will learn more complex examples like traffic sign recognition, fraud detection, text generation and sentiment analysis. You will learn conceptions of deep convolution neural networks, autoencoders, LSTM and GRU networks, word embeddings, word2vec and GloVe.You will implement these conceptions with the usage of different packages which can be used for creating of neural networks and deep learning in R: MXNet, H2O, and Keras with TensorFlow.This book contains a lot of code samples (with downloadable example code files) and it will take you from theory to practice even if you don't yet have hi-level R skills. One important benefit of this book is covering of data pre-processing infrastructure for R. You will not the deep learning conceptions only, but way how to get different datasets (images, texts, series), prepare it for work and preprocess. Generative adversarial networks not covered by this book, but you will receive enough information to implement it yourself using explained R deep learning infrastructure.Authors combined detailed explanations of theory with real-world examples in this book. It will provide you the ability to integrate deep learning capabilities with strong R-language data analysis infrastructure. I think that R Deep Learning Projects is very useful because it shows deep learning applications for real cases.
Amazon Verified review Amazon
epictitus Dec 08, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
this book was immediately useful in my workflowI could use maybe one more word2vec example maybe generating synonyms but that’s a small complaint.
Amazon Verified review Amazon
Bruce W. Klimpke Nov 09, 2019
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This book is a good companion to books that are more theoretical in nature. If you are looking for some real code with in depth explanations on how to solve deep learning problems this is the book for you. The author has a fun writing style and exposes the reader to various AI libraries. The book achieves its goals.
Amazon Verified review Amazon
akshay Oct 06, 2020
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Mrp is actually 899
Amazon Verified review Amazon
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