# Summary v2 * NN
* Perceptron & Two Layer NN
* hidden layer& activation function& softmax
* details
* regularization& dropout
* initialization
* batch normalization
* backpropagation
* gradient vanish& exploding
* Basic CNN
* convolution
* different types of kernel
* identify
* smoothing
* sharpening
* edge
* padding& stride
* layers
* convolution
* max/average pooling
* fully connect
* Non-linearity and ReLU Layer
* Adavance CNN
* LeNet
* AlexNet
* VGG
* NiN
* Multi-Branch Networks(GoogLeNet\&I mageNet)
* Residual Networks(ResNet) and ResNeXt
* Densely Connected Networks(DenseNet)
* Basic RNN
* Seq Model
* AR
* Morkov
* Text to Seq
* tokenization
* vocabulary
* Language Model
* Markov Model & N-grams
* Word Frequency
* Laplace Smoothing
* Perplexity
* Partition Sequence(random sampling& sequential partitioning)
* RNN
* recurrence formula
* what is depth & time
* one to one
* one to many
* many to one
* many to many
* seq to seq(many to one)
* seq to seq(many to one + one to many)
* without hidden state
* with hidden state
* backpropagation through time
* trancate backpropagation through time
* Advance RNN
* Gated Recurrent Units(GRU)
* Long Short-Term Memory(LSTM)
* Bidirectional Recurrent Neural Networks(BRNN)
* Encoder-Decoder Architecture
* Seuqence to Sequence Learning(Seq2Seq)
* Attention Mechanism and Transformers
* Queries, Keys, and Values
* Attention is all you need
* Attention and Kernel
* Attention Scoring Function
* The Bahdanau Attention
* Multi-Head Attention
* Self-Attention
* The Transformer Architecture
* Transformers for NLP
* Transformers for Vision& Multimodal
* LLM Pretraining
* Word Embedding(word2vec)
* Approximate Training
* Word Embedding with Global Vectors(GloVe)
* Encoder(BERT)
* Decoding(GPT\&XLNet\&Lamma)
* Encoder-Decoder(BART& T5)
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