Notes for Prof. Hung-Yi Lee's ML Lecture: RNN
RNN
Notes for Prof. Hung-Yi Lee's ML Lecture: Support Vector Machine
My Intuitive Explanation
Notes for Prof. Hung-Yi Lee's ML Lecture: Transfer Learning
Transfer learning is about the scenarios that we have data which are not directly directly related to the task considered. Besiedes, the usage of terminologies in the field of transfer learning are somewhat chaotic, so the meaning of some terms may vary between articles.
Notes for Prof. Hung-Yi Lee's ML Lecture: Deep Generative Model
Component by Component
Notes for Prof. Hung-Yi Lee's ML Lecture: More about Auto-Encoder
More than minimizing reconstruction error
Notes for Prof. Hung-Yi Lee's ML Lecture 16: Auto-Encoder
By the concept of auto-encoder, we train an NN encoder and an NN decoder simultaneously. The encoder is a function that generate a code by the input, and the decoder is a function that generate an output by a code. We also call the code as the embedding, the latent representation, or the latent code, etc.. To train the encoder and the decoder, we cascade the decoder after the decoder, and train the cascaded neural network to reproduce outputs that are as close as the inputs.
Notes for Prof. Hung-Yi Lee's ML Lecture 15: Neighbor Embedding
Manifold Learning
Notes for Prof. Hung-Yi Lee's ML Lecture 14: Word Embedding
Approaches to Represent Words
Notes for Prof. Hung-Yi Lee's ML Lecture 13: Unsupervised Learning- Linear Methods
Unsupervised Learning
Notes for Prof. Hung-Yi Lee's ML Lecture 12: Semi-Supervised Learning
In supervised learning, all the training data are labelled, i.e. we have