Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other

Anshuman Dalai

Last update: Jan 24, 2022

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Deep Learning ML_Model_implementaion

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ML_Model_implementaion

Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other

dectree_model: Implementation of "Decision tree" algorithm with accurary calculation using confusion matrix and decision tree regressor on iris.csv dataset.

Code accompanying the paper "How Tight Can PAC-Bayes be in the Small Data Regime?"

How Tight Can PAC-Bayes be in the Small Data Regime? This is the code to reproduce all experiments for the following paper: @inproceedings{Foong:2021:

5 Dec 21, 2021

A framework that constructs deep neural networks, autoencoders, logistic regressors, and linear networks

A framework that constructs deep neural networks, autoencoders, logistic regressors, and linear networks without the use of any outside machine learning libraries - all from scratch.

2 Nov 14, 2022

Hand gesture recognition based whiteboard that allows you to write on live webcam. This is the first version and has features like 4 different colors, eraser and a recording option that records your session and saves it in a "recordings" folder. Use index finger to draw and two or more fingers to move around and select items. Future version will contain more functionalities like changeable thickness, color palette, integration with zoom and google meet etc.

hand-write Hand gesture recognition based whiteboard that allows you to write on live webcam. This is the first version and has features like 4 differ

27 Dec 16, 2022

Snapchat-filters-app-opencv-python - Here we used opencv and other inbuilt python modules to create filter application like snapchat

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2 Jul 19, 2022

New AidForBlind - Various Libraries used like OpenCV and other mentioned in Requirements.txt

AidForBlind Recommended PyCharm IDE Various Libraries used like OpenCV and other

1 Jan 13, 2022

A modular, research-friendly framework for high-performance and inference of sequence models at many scales

T5X T5X is a modular, composable, research-friendly framework for high-performance, configurable, self-service training, evaluation, and inference of

1.1k Jan 8, 2023

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

21.8k Jan 9, 2023

Node-level Graph Regression with Deep Gaussian Process Models

Node-level Graph Regression with Deep Gaussian Process Models Prerequests our implementation is mainly based on tensorflow 1.x and gpflow 1.x: python

1 Jan 16, 2022

Clean and readable code for Decision Transformer: Reinforcement Learning via Sequence Modeling

Minimal implementation of Decision Transformer: Reinforcement Learning via Sequence Modeling in PyTorch for mujoco control tasks in OpenAI gym

104 Jan 6, 2023

Owner

Anshuman Dalai

GitHub

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

6.9k Jan 4, 2023

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

5.7k Feb 12, 2021

Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other

Related tags

Overview

ML_Model_implementaion

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Code accompanying the paper "How Tight Can PAC-Bayes be in the Small Data Regime?"

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Owner

Anshuman Dalai

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

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