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ML/DL Notes 0.0.1 documentation
ML
Basic Terms
Decision Trees
Perceptron
Optimizers
Neural Nets
Loss
Regularization
Other Algos
Imbalanced Data
Multiclass
NLP
Embeddings
Neural Embeddings
Similarity Measures
Meta
N Grams
Evaluating LMs
Beam Search
RL
Policy Gradients
Policy Gradient Derivation
Reward to Go
Reward to Go Derivation
Baselines
Unbiased State Value Proof
Math
Moore Penrose Pseudoinverse
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Contents
ML/DL Notes
Indices and tables
ML/DL Notes
Contents
ML/DL Notes
Indices and tables
ML/DL Notes
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Indices and tables
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Index
Module Index
Search Page
ML
Basic Terms
Excercises
Decision Trees
Decision Tree from known features
Ways to split
Pruning
Prepruning
Postpruning
Perceptron
Voted perceptron
Problems
Average weighted perceptron
Perceptron proof
Inspiration
Demonstrating that
\(w_{k+1} * w_*\)
grows at each iteration
Practice
Optimizers
Good old SGD
LR scheduling
Momentum
Adagrad
Adam
Excercises
Neural Nets
Weight initialization
Layer norm
Loss
Binary Cross Entropy
Multiclass Cross Entropy AKA negative log likelihood loss
Excercises
Regularization
Why?
L1
L2
Other Algos
K-means
K nearest neighbor
Imbalanced Data
Subsampling
Weighting
Multiclass
OVA - one versus all
AVA - all versus all
Excercises
NLP
Embeddings
What
Why
How
Term Document Matrix
Word-Word Matrix
TF-IDF
Pointwise Mutual Information
PPMI
Weighted PPMI
Add k PPMI
Bias
Excercises
Neural Embeddings
Skip gram with negative sampling
Weighted unigram sampling
Fasttext
Excercises
Similarity Measures
Cosine
Meta
Zipf’s Law
Evaluating similarity measures
Inter annotator agreement
Basic probability
Cohen’s Kappa
Baselines for word sense prediction
Closed vs Open vocab
Perplexity
Meta
Excercises
N Grams
Smoothing
Laplace Smoothing
Recover adjusted counts
Backoff
Stupid backoff
Evaluating LMs
BLEU
Beam Search
RL
Policy Gradients
What
Why
Explanation
Policy Gradient Derivation
Reward to Go
Reward to Go Derivation
Sources
Baselines
Unbiased State Value Proof
Math
Moore Penrose Pseudoinverse