Machine Learning from scratch

This repository is dedicated to the implementation of machine learning methods and models with a detailed description and visualization

Our site random forest

Classical Machine Learning

Linear Models

1. Linear regression analytical method link
2. Linear regression gradient method link
3. Linear regression Sklearn link
4. Support vector machine (SVM) link
5. Logistic regression link

Decisions trees

1. Decision Tree Regressor link

Ensembles

1. Bagging on decision trees link
2. Gradient Boosting link

Probabilistic methods

1. Naive Bayesianс classifier link
2. Naive Bayesianс classifier sklearn link
3. A little bit about statistical stability link

Deep Learning

Neural Networks

• Fully connected neural network

1. Task about two lines link
2. Task about two lines torch link
3. Classifier handwritten numbers (MNIST) from scratch link
4. Classifier handwritten numbers (MNIST) torch link

• Convolutional neural network

1. Classifier handwritten numbers (MNIST) torch link

Natural language processing

1. word2vec word embeddings code, paper
2. LSTM next word prediction code, paper
3. LSTM text generation code, paper
4. seq2seq machine translation code, paper
5. seq2seq Attention machine translation code, paper
6. BERT code, paper

Computer Vision

• Image clustering link, paper

materials:

ML YSDA

ML ITMO

NLP Lena Voita

ML MIPT

why?

how can you use algorithm without knowing how it works. Writing an algorithm helps me to understand all the nuances of its work, as well as in the next solution of the problem, you will see why it is good to apply one or another algorithm since you fully know their work

Tell me I didnt do it for nothing.

Omar Zoloev, 21. 26.06.2022