Objective of the repository is to learn and build machine learning models using Pytorch.
List of Algorithms Covered
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Day 1 - Linear Regression
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Day 2 - Logistic Regression
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Day 3 - Decision Tree
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Day 4 - KMeans Clustering
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Day 5 - Naive Bayes
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Day 6 - K Nearest Neighbour (KNN)
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Day 7 - Support Vector Machine
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Day 8 - Tf-Idf Model
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Day 9 - Principal Components Analysis
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Day 10 - Lasso and Ridge Regression
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Day 11 - Gaussian Mixture Model
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Day 12 - Linear Discriminant Analysis
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Day 13 - Adaboost Algorithm
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Day 14 - DBScan Clustering
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Day 15 - Multi-Class LDA
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Day 16 - Bayesian Regression
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Day 17 - K-Medoids
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Day 18 - TSNE
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Day 19 - ElasticNet Regression
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Day 20 - Spectral Clustering
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Day 21 - Latent Dirichlet
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Day 22 - Affinity Propagation
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Day 23 - Gradient Descent Algorithm
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Day 24 - Regularization Techniques
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Day 25 - RANSAC Algorithm
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Day 26 - Normalizations
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Day 27 - Multi-Layer Perceptron
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Day 28 - Activations
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Day 29 - Optimizers
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Day 30 - Loss Functions
Let me know if there is any correction. Feedback is welcomed.
SLAM-application: installation and test (3D): LeGO-LOAM, LIO-SAM, and LVI-SAM Tested on Quadruped robot in Gazebo β Results: video, video2 Requirement
β‘ funk-svd funk-svd is a Python 3 library implementing a fast version of the famous SVD algorithm popularized by Simon Funk during the Neflix Prize co
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implementation of machine learning Algorithms such as decision tree and random forest and xgboost on darasets then compare results for each and implement ant colony and genetic algorithms on tsp map,
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