Tutorials

1. What is Neural Network?
   1. Dive into the neuron
   2. How does a neural network simulate an arbitrary function
   3. Why do we need neural networks
2. How to construct a neural network
   1. Fully connected neural network
   2. Use graphical tool to design neural network
   3. The "activation function" of the output layer
3. How to train a neural network
   1. Learning algorithm and principle
   2. Build and train neural networks from scratch
   3. Rewrite the code using PyTorch
   4. Use graphical tool to train neural network
4. Some important problems of neural network
   1. Network structure
   2. Overfitting
   3. Underfitting
   4. Overfitting vs underfitting
   5. Initialization
   6. Vanishing gradient and exploding gradient
5. Convolutional Neural Network(CNN)
   1. 1D-convolution
   2. 1D-convolution experiments
   3. 1D-pooling
   4. 1D-CNN experiments
   5. 2D-CNN
   6. 2D-CNN experiments
6. Recurrent Neural Network(RNN)
   1. Vanilla RNN
   2. Seq2seq, Autoencoder, Encoder-Decoder
   3. Advanced RNN
   4. RNN classification experiment
7. Natural language processing
   1. Embedding: Convert symbols to values
   2. Text Classification 1
   3. Text Classification 2
   4. TextCNN
   5. Entity recognition
   6. Word segmentation, POS tagging and chunking
   7. Sequence tagging in action
   8. Bidirectional RNN
   9. BI-LSTM-CRF
   10. Attention
8. Language Models
   1. n-gram Model: Unigram
   2. n-gram Model: Bigram
   3. n-gram Model: Trigram
   4. RNN Language Model
   5. Transformer Language Model
9. Linear Algebra
   1. Vector
   2. Matrix
   3. Dive in matrix multiplication
   4. Tensor