Deep Learning with R
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VIDEO TUTORIAL Deep Learning with R

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Optimize Algorithms and achieve greater levels of accuracy with Deep learning

About This Deep Learning R video Course

  • Explore and create intelligent systems using Deep learning techniques
  • Understand the usage of multiple applications like Natural Language Processing, Bioinformatics, Recommendation Engines, etc. where deep learning models are implemented
  • Get hands on with various Deep Learning scenarios and get mind blowing insights from your data

Deep Learning course In Detail

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 video 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.

What will you learn in this course?

Course plan
Chapter 1
Introduction to Deep Learning
Chapter 2
Working with Neural Network Architectures
Chapter 3
Advanced Artificial Neural Networks
Chapter 4
Convolutional Neural Networks
Chapter 5
Recurrent Neural Networks
Chapter 6
Towards Unsupervised and Reinforcement Learning
Chapter 7
Applications of Deep Learning

Detailed course plan

Chapter 1 : Introduction to Deep Learning
Lesson 1The Course Overview
Lesson 2Fundamental Concepts in Deep Learning
Lesson 3Introduction to Artificial Neural Networks
Lesson 4Classification with Two-Layers Artificial Neural Networks
Lesson 5Probabilistic Predictions with Two-Layer ANNs
Chapter 2 : Working with Neural Network Architectures
Lesson 1Introduction to Multi-hidden-layer Architectures
Lesson 2Tuning ANNs Hyper-Parameters and Best Practices
Lesson 3Neural Network Architectures
Lesson 4Neural Network Architectures (Continued)
Chapter 3 : Advanced Artificial Neural Networks
Lesson 1The LearningProcess
Lesson 2Optimization Algorithms and Stochastic Gradient Descent
Lesson 3Backpropagation
Lesson 4Hyper-Parameters Optimization
Chapter 4 : Convolutional Neural Networks
Lesson 1Introduction to Convolutional Neural Networks
Lesson 2Introduction to Convolutional Neural Networks (Continued)
Lesson 3CNNs in R
Lesson 4Classifying Real-World Images with Pre-Trained Models
Chapter 5 : Recurrent Neural Networks
Lesson 1Introduction to Recurrent Neural Networks
Lesson 2Introduction to Long Short-Term Memory
Lesson 3RNNs in R
Lesson 4Use-Case – Learning How to Spell English Words from Scratch
Chapter 6 : Towards Unsupervised and Reinforcement Learning
Lesson 1Introduction to Unsupervised and Reinforcement Learning
Lesson 2Autoencoders
Lesson 3Restricted Boltzmann Machines and Deep Belief Networks
Lesson 4Reinforcement Learning with ANNs
Lesson 5Use-Case – Anomaly Detection through Denoising Autoencoders
Chapter 7 : Applications of Deep Learning
Lesson 1Deep Learning for Computer Vision
Lesson 2Deep Learning for Natural Language Processing
Lesson 3Deep Learning for Audio Signal Processing
Lesson 4Deep Learning for Complex Multimodal Tasks
Lesson 5Other Important Applications of Deep Learning
Chapter 8 : Advanced Topics
Lesson 1Debugging Deep Learning Systems
Lesson 2GPU and MGPU Computing for Deep Learning
Lesson 3A Complete Comparison of Every DL Packages in R
Lesson 4Research Directions and Open Questions

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