Add classical/new architectures

[omarsar]

Some ideas for figures to add to the PPT

• Linear regression, single-layer neural network
• Multilayer Perceptron with hidden layer
• Backpropagation
• Batch Normalization and alternatives
• Computational Graphs
• Dropout
• CNN - padding, stride, pooling,...
• LeNet
• AlexNet
• VGG
• GoogleNet
• ResNet
• DenseNet
• Memory Networks
• RNN
• Deep RNN
• Bidirectional RNN
• GRU
• LSTM
• Language RNN models
• Backpropagation through time
• Encoder-Decoder Architecture
• Seq2seq with RNN encoder-decoder
• Bearm search and other decoding strategies
• Attention
• Multi-head attention
• Self-attention
• Transformer
• Word2vec/GloVe/Skip-gram/CBOW/BERT/GPT....
• Common/Popular CV/NLP Tasks

List adopted from multiple resources including nlpoverview and d2l.ai which both contain a very solid syllabus.

Please feel free to make suggestions below. If you would like to help, also let me know.

[heany]

Maybe, the generative adversarial networks and graph neural networks should be included in the list.

[narutomst]

Deep Belief Networks

[nashid]

Can we Graph Neural Network models like GCN, GAT, GGNN, GraphSage, etc?

[nashid]

Including Contrastive Representation Learning models would be great!