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Deep Learning with R
Introduction to Deep Learning
The Course Overview (5:22)
Fundamental Concepts in Deep Learning (7:42)
Introduction to Artificial Neural Networks (7:57)
Classification with Two-Layers Artificial Neural Networks
Probabilistic Predictions with Two-Layer ANNs (6:32)
Working with Neural Network Architectures
Introduction to Multi-hidden-layer Architectures (4:31)
Tuning ANNs Hyper-Parameters and Best Practices (6:12)
Neural Network Architectures (4:57)
Neural Network Architectures (Continued) (8:02)
Advanced Artificial Neural Networks
The LearningProcess (5:35)
Optimization Algorithms and Stochastic Gradient Descent (8:11)
Backpropagation (6:44)
Hyper-Parameters Optimization (7:17)
Convolutional Neural Networks
Introduction to Convolutional Neural Networks (8:17)
Introduction to Convolutional Neural Networks (Continued) (6:12)
CNNs in R (4:47)
Classifying Real-World Images with Pre-Trained Models (8:29)
Recurrent Neural Networks
Introduction to Recurrent Neural Networks (11:57)
Introduction to Long Short-Term Memory (8:07)
RNNs in R (8:55)
Use-Case – Learning How to Spell English Words from Scratch (6:34)
Towards Unsupervised and Reinforcement Learning
Introduction to Unsupervised and Reinforcement Learning (6:44)
Autoencoders (4:56)
Restricted Boltzmann Machines and Deep Belief Networks (7:44)
Reinforcement Learning with ANNs (7:22)
Use-Case – Anomaly Detection through Denoising Autoencoders (6:52)
Applications of Deep Learning
Deep Learning for Computer Vision (7:19)
Deep Learning for Natural Language Processings (6:04)
Deep Learning for Audio Signal Processing (5:01)
Deep Learning for Complex Multimodal Tasks (4:32)
Other Important Applications of Deep Learning (5:24)
Advanced Topics
Debugging Deep Learning Systems (5:56)
GPU and MGPU Computing for Deep Learning (4:57)
A Complete Comparison of Every DL Packages in R (4:40)
Research Directions and Open Questions (4:47)
Codes
S1.3
S1.4
S2.4
S3.4
S5.3
S2.3
S5.4
S6.5
Use-Case – Anomaly Detection through Denoising Autoencoders
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