Deep Learning with R [Video]

Preview in Mapt

Deep Learning with R [Video]

Vincenzo Lomonaco

1 customer reviews
Optimize Algorithms and achieve greater levels of accuracy with Deep learning
Mapt Subscription
FREE
$29.99/m after trial
Video
$10.00
RRP $124.99
Save 91%
What do I get with a Mapt Pro subscription?
  • Unlimited access to all Packt’s 5,000+ eBooks and Videos
  • Early Access content, Progress Tracking, and Assessments
  • 1 Free eBook or Video to download and keep every month after trial
What do I get with an eBook?
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with Print & eBook?
  • Get a paperback copy of the book delivered to you
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with a Video?
  • Download this Video course in MP4 format
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
$0.00
$10.00
$29.99 p/m after trial
RRP $124.99
Subscription
Video
Start 30 Day Trial

Frequently bought together


Deep Learning with R [Video] Book Cover
Deep Learning with R [Video]
$ 124.99
$ 10.00
Applied Machine Learning and Deep Learning with R [Video] Book Cover
Applied Machine Learning and Deep Learning with R [Video]
$ 124.99
$ 10.00
Buy 2 for $20.00
Save $229.98
Add to Cart

Video Details

ISBN 139781786467416
Course Length4 hours and 4 minutes

Video Description

Deep learning refers to artificial neural networks that are composed of many layers. Deep learning is a powerful set of techniques for finding accurate information from raw data.

This tutorial will teach you how to leverage deep learning to make sense of your raw data by exploring various hidden layers of data. Each section in this course provides a clear and concise introduction of a key topic, one or more example of implementations of these concepts in R, and guidance for additional learning, exploration, and application of the skills learned therein. You will start by understanding the basics of Deep Learning and Artificial neural Networks and move on to exploring advanced ANN’s and RNN’s. You will deep dive into Convolutional Neural Networks and Unsupervised Learning. You will also learn about the applications of Deep Learning in various fields and understand the practical implementations of Scalability, HPC and Feature Engineering.

Starting out at a basic level, users will be learning how to develop and implement Deep Learning algorithms using R in real world scenarios.

Style and Approach

This video lecture series simplifies otherwise incredibly dense topics with clear, concise explanations and reproducible, hands-on examples. No prior knowledge of deep learning is assumed, but learners gain intermediate proficiency by the end of the course.

Table of Contents

Introduction to Deep Learning
The Course Overview
Fundamental Concepts in Deep Learning
Introduction to Artificial Neural Networks
Classification with Two-Layers Artificial Neural Networks
Probabilistic Predictions with Two-Layer ANNs
Working with Neural Network Architectures
Introduction to Multi-hidden-layer Architectures
Tuning ANNs Hyper-Parameters and Best Practices
Neural Network Architectures
Neural Network Architectures (Continued)
Advanced Artificial Neural Networks
The Learning Process
Optimization Algorithms and Stochastic Gradient Descent
Backpropagation
Hyper-Parameters Optimization
Convolutional Neural Networks
Introduction to Convolutional Neural Networks
Introduction to Convolutional Neural Networks (Continued)
CNNs in R
Classifying Real-World Images with Pre-Trained Models
Recurrent Neural Networks
Introduction to Recurrent Neural Networks
Introduction to Long Short-Term Memory
RNNs in R
Use-Case – Learning How to Spell English Words from Scratch
Towards Unsupervised and Reinforcement Learning
Introduction to Unsupervised and Reinforcement Learning
Autoencoders
Restricted Boltzmann Machines and Deep Belief Networks
Reinforcement Learning with ANNs
Use-Case – Anomaly Detection through Denoising Autoencoders
Applications of Deep Learning
Deep Learning for Computer Vision
Deep Learning for Natural Language Processing
Deep Learning for Audio Signal Processing
Deep Learning for Complex Multimodal Tasks
Other Important Applications of Deep Learning
Advanced Topics
Debugging Deep Learning Systems
GPU and MGPU Computing for Deep Learning
A Complete Comparison of Every DL Packages in R
Research Directions and Open Questions

What You Will Learn

  • Learn the basics of Deep Learning and Artificial Neural Networks
  • Understand classification and probabilistic predictions with Single-hidden-layer Neural Networks
  • Increase your expertise by covering intermediate and advanced Artificial and Recurrent Neural Networks
  • Get to grips with Convolutional and Deep Belief Networks
  • Learn practical applications of Deep Learning
  • Learn about Feature Engineering and Multicore/Cluster Computing

Authors

Table of Contents

Introduction to Deep Learning
The Course Overview
Fundamental Concepts in Deep Learning
Introduction to Artificial Neural Networks
Classification with Two-Layers Artificial Neural Networks
Probabilistic Predictions with Two-Layer ANNs
Working with Neural Network Architectures
Introduction to Multi-hidden-layer Architectures
Tuning ANNs Hyper-Parameters and Best Practices
Neural Network Architectures
Neural Network Architectures (Continued)
Advanced Artificial Neural Networks
The Learning Process
Optimization Algorithms and Stochastic Gradient Descent
Backpropagation
Hyper-Parameters Optimization
Convolutional Neural Networks
Introduction to Convolutional Neural Networks
Introduction to Convolutional Neural Networks (Continued)
CNNs in R
Classifying Real-World Images with Pre-Trained Models
Recurrent Neural Networks
Introduction to Recurrent Neural Networks
Introduction to Long Short-Term Memory
RNNs in R
Use-Case – Learning How to Spell English Words from Scratch
Towards Unsupervised and Reinforcement Learning
Introduction to Unsupervised and Reinforcement Learning
Autoencoders
Restricted Boltzmann Machines and Deep Belief Networks
Reinforcement Learning with ANNs
Use-Case – Anomaly Detection through Denoising Autoencoders
Applications of Deep Learning
Deep Learning for Computer Vision
Deep Learning for Natural Language Processing
Deep Learning for Audio Signal Processing
Deep Learning for Complex Multimodal Tasks
Other Important Applications of Deep Learning
Advanced Topics
Debugging Deep Learning Systems
GPU and MGPU Computing for Deep Learning
A Complete Comparison of Every DL Packages in R
Research Directions and Open Questions

Video Details

ISBN 139781786467416
Course Length4 hours and 4 minutes
Read More
From 1 reviews

Read More Reviews

Recommended for You

Applied Machine Learning and Deep Learning with R [Video] Book Cover
Applied Machine Learning and Deep Learning with R [Video]
$ 124.99
$ 10.00
Getting Started with Deep Learning with R [Integrated Course] Book Cover
Getting Started with Deep Learning with R [Integrated Course]
$ 124.99
$ 10.00
Eder Santana's Deep Learning with Python Book Cover
Eder Santana's Deep Learning with Python
$ 27.99
$ 10.00
Deep Learning with Microsoft Cognitive Toolkit Book Cover
Deep Learning with Microsoft Cognitive Toolkit
$ 39.99
$ 10.00
Reinforcement Learning with R Book Cover
Reinforcement Learning with R
$ 35.99
$ 10.00
Full Stack Development with JHipster Book Cover
Full Stack Development with JHipster
$ 35.99
$ 10.00