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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...

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.

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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Instant access to your digital eBook copy whilst your Print order is Shipped

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A. Albert Mar 18, 2018

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.

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Andrii Apr 25, 2018

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.

epictitus Dec 08, 2018

this book was immediately useful in my workflowI could use maybe one more word2vec example maybe generating synonyms but that’s a small complaint.

Bruce W. Klimpke Nov 09, 2019

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.

akshay Oct 06, 2020

Mrp is actually 899

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

How is deep learning applied in self-driving cars?

Traffic sign recognition using CNN

Getting started with exploring GTSRB

Dealing with a small training set – data augmentation

Reviewing methods to prevent overfitting in CNNs

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