Deep Learning with R
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Deep Learning with R

Optimize Algorithms and achieve greater levels of accuracy with Deep learning
3.8 (10 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
79 students enrolled
Created by Packt Publishing
Last updated 5/2017
English
Current price: $10 Original price: $125 Discount: 92% off
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Includes:
  • 4 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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
View Curriculum
Requirements
  • Familiarity with the theoretical underpinnings of neutral networks is highly useful, this course is appropriate for anyone with prior experience in R and a general familiarity with predictive models.
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.

About the Author

Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna. He is also the PhD students' representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. Previously, he was a Machine Learning software engineer at IDL in-line Devices and a Master Student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: A comparison between CNNs and HTMs on object recognition tasks".

Who is the target audience?
  • This course is for anyone with an interest in creating cutting-edge deep learning models in R.
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Curriculum For This Course
35 Lectures
04:04:29
+
Introduction to Deep Learning
5 Lectures 35:36

This video provides an overview of the entire course.

Preview 05:22

The main objective is to understand the fundamental concepts and key features that make it so special and different from the classical Machine Learning approach.

Fundamental Concepts in Deep Learning
07:42

The goal of this video is to learn more about Artificial Neural Networks and their vast world of variations, explore the basic architectures of ANNs in detail and talk about their possible implementations in R.

Introduction to Artificial Neural Networks
07:57

Applying what you have learned about the Multilayer Perceptron algorithm to a real-world application, which classifies handwritten digits in images.

Classification with Two-Layers Artificial Neural Networks
08:02

To get probabilistic predictions using Artificial Neural Networks and specifically in the context of a multi-class classification problem.

Probabilistic Predictions with Two-Layer ANNs
06:33
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Working with Neural Network Architectures
4 Lectures 23:42

To add multiple hidden layers to the basic Multilayers Perceptron algorithm in order to build more complex models of the world and increase the accuracy of our predictions.

Preview 04:31

The goal of this video is to learn the best practices for tuning the hyper-parameters of an ANN and being able to generalize well on the data we have never seen before. This would be the latest essential skill to acquire in order to get the best out of our ANN solution.

Tuning ANNs Hyper-Parameters and Best Practices
06:12

The goal of this video is to learn more about Multi-hidden-layer Neural Networks and how to use them in order to solve the practical problem of classifying handwritten digits within the R language.

Neural Network Architectures
04:57

The goal of this video is to apply what we have learned about Multi-hidden-layer ANNs to a new real-world problem and get more confidence in the use of the H2O package.

Neural Network Architectures (Continued)
08:02
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Advanced Artificial Neural Networks
4 Lectures 27:47
The main objective is to understand the optimization process behind and common to every Deep Learning model with a more formal definition with respect to what was previously introduced.
Preview 05:35

To explore with more details, the most common algorithm to minimize the loss function called Stochastic Gradient Descent.

Optimization Algorithms and Stochastic Gradient Descent
08:11

The goal of this video is to understand how to actually learn the weights of our Deep Learning model using Stochastic Gradient Descent through Backpropagation, the standard way of computing the gradient for Artificial Neural Networks.

Backpropagation
06:44

To get to tune the hyper-parameters automatically in order to minimize the error on the validation set.

Hyper-Parameters Optimization
07:17
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Convolutional Neural Networks
4 Lectures 39:42

This first video, will be an introduction to the fundamental concepts behind Convolutional Neural Networks. The main objective of this video is to motivate their use highlighting the differences from classical feed-forward neural networks.

Preview 09:57

The goal of this video is to learn more about Convolutional Neural Networks, concluding our dissertation on the layer-wise structure of a CNN and understand how to design architecture suitable for your specific problem.

Introduction to Convolutional Neural Networks (Continued)
10:35

The aim of this video is to understand how to actually implement CNNs in R, and use it to solve real-world problems.

CNNs in R
10:41

The goal of this video is to learn about the concept of transfer Learning, and how we can use and exchange DL pre-trained models to solve even new tasks with a very tiny computational overhead.

Classifying Real-World Images with Pre-Trained Models
08:29
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Recurrent Neural Networks
4 Lectures 35:34

The aim of this video is to introduce the fundamental concepts behind Recurrent Neural Networks. The main objective is to underline their main differences from classical feed-forward neural networks and CNNs.

Preview 11:57

The aim of this video is to learn more about a specific type of Recurrent Neural Networks, called Long Short-Term Memories, a natural extension of classical RNNs for dealing with long-term dependencies.

Introduction to Long Short-Term Memory
08:07

The aim of this video is to understand how to actually implement RNNs in R, and use it to solve real-world problems.

RNNs in R
08:55

The aim of this video is to learn how to train and use an LSTM to solve a complex problem like predicting the next character in a sentence given the occurrences of the previous characters.

Use-Case – Learning How to Spell English Words from Scratch
06:35
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Towards Unsupervised and Reinforcement Learning
5 Lectures 33:38

The aim of this video is to understand the main differences from classical supervised learning and how they can be combined together.

Preview 06:44

In this video, you will learn more about a specific unsupervised learning algorithm called Autoencoders. This type of Artificial Neural Networks are simple and effective solutions for learning efficient representation of data without any supervision.

Autoencoders
04:56

The aim of this video is to learn about two very important unsupervised Deep Learning algorithms for features hierarchies: Restricted Boltzmann Machines and Deep Belief Networks.
Restricted Boltzmann Machines and Deep Belief Networks
07:44

The aim of this video is to get a quick picture on the main approaches for solving reinforcement learning tasks with Deep Learning.

Reinforcement Learning with ANNs
07:22

The aim of this video is to learn how to train and use an Autoencoders in R with the H2O package, for solving a real-world anomaly detection task.

Use-Case – Anomaly Detection through Denoising Autoencoders
06:52
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Applications of Deep Learning
5 Lectures 28:20

The aim of this video is to spark new inspiration for creatively applying Deep Learning techniques to real-world problems in Computer Vision.

Preview 07:19

The aim of this video is to creatively apply Deep Learning techniques to real-world problems in Natural Language Processing (NLP).

Deep Learning for Natural Language Processings
06:04

In this video, we'll creatively apply Deep Learning techniques to real-world problems in ASP.

Deep Learning for Audio Signal Processing
05:01

The aim of this video is to introduce some of the most successful applications of Deep Learning for complex multimodal tasks.

Deep Learning for Complex Multimodal Tasks
04:32

In this video, let's take a look at some of the most successful applications in other fields we didn't mention before.

Other Important Applications of Deep Learning
05:24
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Advanced Topics
4 Lectures 20:10

The aim of this first video is to learn how to deal with models which do not behave as they should.

Preview 05:56

In this video, you will learn how to speed-up the training and deploy complex DL models.

GPU and MGPU Computing for Deep Learning
04:57

The aim of this video is to present a complete overview on every available R package for Deep Learning and Neural Networks.

A Complete Comparison of Every DL Packages in R
04:40

In this video, you will learn about the most interesting research directions and open question for the long-term developments of Deep Learning toward truly intelligent agents.

Research Directions and Open Questions
04:37
About the Instructor
Packt Publishing
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