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

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

References

  • Sklearn Library
  • ML-Glossary
  • ML From Scratch (Github)
You might also like...
Official implementation of
Official implementation of "A Unified Objective for Novel Class Discovery", ICCV2021 (Oral)

A Unified Objective for Novel Class Discovery This is the official repository for the paper: A Unified Objective for Novel Class Discovery Enrico Fini

Code for the paper Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations (AKBC 2021).
Code for the paper Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations (AKBC 2021).

Relation Prediction as an Auxiliary Training Objective for Knowledge Base Completion This repo provides the code for the paper Relation Prediction as

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations Requirements The code is implemented in Python and requires

v objective diffusion inference code for JAX.

v-diffusion-jax v objective diffusion inference code for JAX, by Katherine Crowson (@RiversHaveWings) and Chainbreakers AI (@jd_pressman). The models

Implementation of a protein autoregressive language model, but with autoregressive infilling objective (editing subsequences capability)
Implementation of a protein autoregressive language model, but with autoregressive infilling objective (editing subsequences capability)

Protein GLM (wip) Implementation of a protein autoregressive language model, but with autoregressive infilling objective (editing subsequences capabil

Paaster is a secure by default end-to-end encrypted pastebin built with the objective of simplicity.
Paaster is a secure by default end-to-end encrypted pastebin built with the objective of simplicity.

Follow the development of our desktop client here Paaster Paaster is a secure by default end-to-end encrypted pastebin built with the objective of sim

A set of tools for creating and testing machine learning features, with a scikit-learn compatible API

Feature Forge This library provides a set of tools that can be useful in many machine learning applications (classification, clustering, regression, e

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

This is the Vowpal Wabbit fast online learning code. Why Vowpal Wabbit? Vowpal Wabbit is a machine learning system which pushes the frontier of machin

We present a framework for training multi-modal deep learning models on unlabelled video data by forcing the network to learn invariances to transformations applied to both the audio and video streams.

Multi-Modal Self-Supervision using GDT and StiCa This is an official pytorch implementation of papers: Multi-modal Self-Supervision from Generalized D

Comments
  • video link for this repository

    video link for this repository

    Is there a youtube video for this repository or is this a self-study course? If there is a video link, can someone please post it as a reply?

    Thank you!

    opened by The-Coding-Kid 1
Owner
Mayur
Waiting for Robot Uprising !
Mayur
Pytorch implementation of "MOSNet: Deep Learning based Objective Assessment for Voice Conversion"

MOSNet pytorch implementation of "MOSNet: Deep Learning based Objective Assessment for Voice Conversion" https://arxiv.org/abs/1904.08352 Dependency L

null 9 Nov 18, 2022
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Machine Learning From Scratch About Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose

Erik Linder-NorΓ©n 21.8k Jan 9, 2023
This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.

Reinforcement-trading This project uses Reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can

Deepender Singla 1.4k Dec 22, 2022
An easy way to build PyTorch datasets. Modularly build datasets and automatically cache processed results

EasyDatas An easy way to build PyTorch datasets. Modularly build datasets and automatically cache processed results Installation pip install git+https

Ximing Yang 4 Dec 14, 2021
Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.

Algo-ScriptML Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The goal of this project is not t

Algo Phantoms 81 Nov 26, 2022
[ICML 2020] Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

PG-MORL This repository contains the implementation for the paper Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Contro

MIT Graphics Group 65 Jan 7, 2023
Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

Ng Kam Woh 71 Dec 22, 2022
Multi-objective gym environments for reinforcement learning.

MO-Gym: Multi-Objective Reinforcement Learning Environments Gym environments for multi-objective reinforcement learning (MORL). The environments follo

Lucas Alegre 74 Jan 3, 2023
This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Developed By Google!

Machine Learning Hand Detector This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Dev

Popstar Idhant 3 Feb 25, 2022
Exact Pareto Optimal solutions for preference based Multi-Objective Optimization

Exact Pareto Optimal solutions for preference based Multi-Objective Optimization

Debabrata Mahapatra 40 Dec 24, 2022