Course on deep learning and it's applications in Computer Vision, Natural Language Processing
week01: Introductory lecture about the course and the basics of Machine Learning.
week02: started tutorial on basics of numpy
week03: lecture on SGD.
week04: started tutortial on optimizers
week05: lecture on backprop
week06: started numpy net tutorial
week07: lecture on basics of deep learning in computer vision
week08: Deep Learning in NLP, Word Embeddings, Word2vec
week09: RNNs/GRU/LSTMs
week10: seminar, knowledge test
week11: lecture on transfer learning and attention
week12: Practice, NER/HuggingFace
- The Elements of Statistical Learning - Stanford University by Trevor Hastie, Robert Tibshirani, Jerome Friedman
- Machine Learning Yearning by Andrew Ng
- Review of linear methods by ODS community