MLWithPytorch

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Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

MLWithPytorch 최신버젼 다운로드

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## Machine Learning Using Pytorch

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

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**List of Algorithms Covered**

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

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

## References

* Sklearn Library
* ML-Glossary
* ML From Scratch (Github)

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