Deep Learning

Description

• Recalls on Neural Networks, Multi Layer Perceptrons, Backpropagation
• Loss functions, Hyperparameter tuning, Regularization, Model selection, weight decay, dropout, Optimization (SGD, Rprop, adam, rmsprop)
• Deep Neural Networks
• Convolutional Neural Networks (CNN), LeNet/AlexNet, Deep Residual Networks (ResNet). Application sof CNNs (Single-Image Super-Resolution, Object detection)
• CNN variations and other solutions for object detection: R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, SSD, YOLO
• Recurrent Neural Networks, Long Short-Term Memory Networks, Gated Recurrent Units, Bidirectional LSTM
• Transformers, sequence-to-sequence (seq2seq) learning, attention
• Generative Models. Restricted Boltzman Machines, Deep Boltzman Machines, Deep Belief Networks). Autoencoders, Stacked (Denoising AutoEncoders), Variational Autoencoders. Generative Adversarial Networks.