30 Days Of Machine Learning Using Pytorch

Overview

MLWithPyTorch

30 Days Of Machine Learning Using Pytorch

Objective of the repository is to learn and build machine learning models using Pytorch.

List of Algorithms Covered

πŸ“Œ Day 1 - Linear Regression
πŸ“Œ Day 2 - Logistic Regression
πŸ“Œ Day 3 - Decision Tree
πŸ“Œ Day 4 - KMeans Clustering
πŸ“Œ Day 5 - Naive Bayes
πŸ“Œ Day 6 - K Nearest Neighbour (KNN)
πŸ“Œ Day 7 - Support Vector Machine
πŸ“Œ Day 8 - Tf-Idf Model
πŸ“Œ Day 9 - Principal Components Analysis
πŸ“Œ Day 10 - Lasso and Ridge Regression
πŸ“Œ Day 11 - Gaussian Mixture Model
πŸ“Œ Day 12 - Linear Discriminant Analysis
πŸ“Œ Day 13 - Adaboost Algorithm
πŸ“Œ Day 14 - DBScan Clustering
πŸ“Œ Day 15 - Multi-Class LDA
πŸ“Œ Day 16 - Bayesian Regression
πŸ“Œ Day 17 - K-Medoids
πŸ“Œ Day 18 - TSNE
πŸ“Œ Day 19 - ElasticNet Regression
πŸ“Œ Day 20 - Spectral Clustering
πŸ“Œ Day 21 - Latent Dirichlet
πŸ“Œ Day 22 - Affinity Propagation
πŸ“Œ Day 23 - Gradient Descent Algorithm
πŸ“Œ Day 24 - Regularization Techniques
πŸ“Œ Day 25 - RANSAC Algorithm
πŸ“Œ Day 26 - Normalizations
πŸ“Œ Day 27 - Multi-Layer Perceptron
πŸ“Œ Day 28 - Activations
πŸ“Œ Day 29 - Optimizers
πŸ“Œ Day 30 - Loss Functions

Let me know if there is any correction. Feedback is welcomed.

References

  • Sklearn Library
  • ML-Glossary
  • ML From Scratch (Github)
Owner
Mayur
Waiting for Robot Uprising !
Mayur
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