akashjaswal

'''
Logistic Regression - Vectorized Implementation w/ Numpy

Setup:
  - features X = Feature Vector of shape (m, n) [Could append bias term to feature matrix with ones(m, 1)]
  - Target y = discrete variable - shape (m, 1) (Consider Binary for now)
  - Weights = Weight matrix of shape (n, 1) - initialize with zeros
  - Standardize features to have zero mean and unit variance.

Gradient Descent Algorithm:

akashjaswal

'''
Linear Regression - Vectorized Implementation w/ Numpy

Setup: 

  - features X = Feature Vector of shape (m, n) [Could append bias term to feature matrix with ones(m, 1)]
  - Target y = continuous variable - shape (m, 1)

  - Weights = Weight matrix of shape (n, 1) - initialize with zeros
  - Standardize features to have zero mean and unit variance. 

akashjaswal

import re
from collections import Counter, defaultdict


def build_vocab(corpus: str) -> dict:
    """Step 1. Build vocab from text corpus"""

    # Separate each char in word by space and add mark end of token
    tokens = [" ".join(word) + " </w>" for word in corpus.split()]