CS 230 ― Deep Learning
My twin brother Afshine
created this set of illustrated Deep Learning cheatsheets covering the content of the CS 230 class, which I TA-ed in Winter 2019 at Stanford. They can (hopefully!) be useful to all future students of this course as well as to anyone else interested in Deep Learning.
Would you like to see this set of cheatsheets in your native language? You can help us translating them on GitHub!
- • Types of layer, filter hyperparameters, activation functions
- • Object detection, face verification and recognition
- • Neural style transfer, architectures using computational tricks
- • Vanishing/exploding gradient, GRU, LSTM, variants of RNNs
- • Word2vec, skip-gram, negative sampling, GloVe, attention model
- • Language model, beam search, Bleu score
- • Data augmentation, batch normalization, regularization
- • Xavier initialization, transfert learning, adaptive learning rates
- • Overfitting small batch, gradient checking