Machine Learning Guide
A machine learning podcast teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models, neural networks, math, languages, frameworks, etc. Where visual ML resources provide the trees, I provide the forest. Podcasts are a great supplement during exercise, commute, chores, etc.
MLG 019 Natural Language Processing 2
Jul 10, 2017
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Natural Language Processing classical/shallow algorithms.
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Resources
Resources best viewed here
Speech and Language Processing
Natural Language Processing - Stanford University
CS224N: Natural Language Processing with Deep Learning | Winter 2019
Show Notes
- Edit distance: Levenshtein distance
- Stemming/lemmatization: Porter Stemmer
- N-grams, Tokens: regex
- Language models
- Machine translation, spelling correction, speech recognition
- Classification / Sentiment Analysis: SVM, Navie bayes
- Information Extraction (POS, NER): Models: MaxEnt, Hidden Markov Models (HMM), Conditional Random Fields (CRF)
- Generative vs Discriminitive models
- Generative: HMM, Bayes, LDA
- Discriminative: SVMs, MaxEnt / LogReg, ANNs
- Pros/Cons
- Generative depends on fewer data (NLP tends to be few data)
- MaxEnt vs Naive Bayes: Independence assumption of Bayes, etc ("Hong" "Kong")
- Topic Modeling and keyword extraction: Latent Dirichlet Allocation (LDA)
- LDA ~= LSA ~= LSI: Latent diriclet allocation, latent semantic indexing, latent semantic analysis
- Search / relevance / document-similarity: Bag-of-words, TF-IDF
- Similarity: Jaccard, Cosine, Euclidean