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Deep Learning with R

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
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
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