A 100 Day ML Challenge to learn and implement ML/DL concepts ranging from the basics of Machine Learning to advanced Deep Learning.

Artificial intelligence (AI, ML, DL) performance metrics implemented in Python

An implementation of ML and DL algorithms from scratch in python using nothing but NumPy and Matplotlib.

A 100 Day ML Challenge to learn and implement ML/DL concepts ranging from the basics of Machine Learning to advance Machine Learning.

This repository contains the ML and DL projects I implemented during my Bachelor's degree. It reflects the knowledge I gained from books and online sources. The models are trained on problems in NLP, Computer Vision, and more. This repository highlights my learning journey and the variety of projects I completed during my Bachelor's studies.

Some basic AI/ML/DL algorithms implemented from scratch for understanding purposes.

This library implements various metrics (including Kaggle Competition, Medicine) for evaluating ML, DL, AI models, and algorithms. 📐📊📈📉📏

This repository contains implementations of various neural networks from scratch (without using ML and DL Libraries). Each implementation is designed to provide a fundamental understanding of how these networks work.

simple implementations of DL ML Matrix and DataStructures

ML/DL/NLP/RL algorithms implemented in Brainfuck.

100 Day ML Challenge to learn and implement ML/DL concepts ranging from the basics to more advanced state of the art models.

6th Semester Mini Project — Emotion Recognition using EEG Signals. This project implements ML/DL algorithms to extract features from EEG brainwave data and classify emotional states, with applications in mental health monitoring and human-computer interaction.

Implementation of machine learning and deep learning algorithms, projects.

A comprehensive collection of implementations for popular ML/DL algorithms with ready-to-run code examples and datasets.

I have worked on an android security project as a consultant for implementing Machine Learning and Deep Learning models for android malware detection The Machine Learning model was developed with Random Forest (RF), Logistic Regression, and Support Vector Machine (SVM) Classifiers The Deep Learning model was developed using Google’s Transformer based Masking model ‘BERT’ and MLP (Multilayer Perceptron) I evaluated the performance difference between ML and DL classifiers in detecting zero-day attacks and compared the results with state-of-the-art methods

Build several DL model from scratch, topics include LR, DNN, CNN, ResNet, VGG, Siamese NN, Yolo, optimizers, initialization methods and regularization.

This is an artificial intelligence (ML and DL) project for network security which works by detecting threats and uses attack classification and then implements self-healing mechanisms

Implementation of a Mobile Fitness Tracker using MatLab and the Classification Learner App. It integrates ML techniques to classify user activities based on accelerometer data. Developed in a MathWorks workshop with SASTRA University, covering MatLab, ML, and DL aspects.

ML / DL Algorithms implemented from scratch. Developed with only numpy as dependency. Machine Learning Algorithms such as Support Vector Machine, Linear Regression, Artificial Neural Networks and other data transformation algorithms are implemented. Project is released as a python package and can be download from Python Package Installer.

自己在学习看论文博客过程中想要实现的一些主流算法,顺带手用tensorflow,keras,pytorch都实现一下,练习一下这些框架的使用.

This boot camp provides a hands-on introduction to AI, ML, and DL, helping participants build foundational skills and understand current advancements. Learners will implement basic ML/DL programs in Python and apply their knowledge to real-world scenarios in a collaborative environment.

This repository gives beginners and newcomers in the field of AI and ML a chance to understand the inner workings of popular learning algorithms by presenting them with a simple way to analyze the implementation of ML and DL algorithms in pure python using only numpy as a backend for linear algebraic computations.

A collection of implemented papers from the DL/ML field.

Self implementation of Device-Level Balance Loss and Communication Balance Loss of DeepSeek v2 Tech Report（Not Given in Official Code）

implements neural network from scratch without using any ml/dl libraries

This repo is about the tech stack needed to be a data scientist, it mainly focus on the self-implementation of basic ML/DL models and their application using related python packages

Efficient ML/DL implementations across multiple domains with K3s multi-node training setup

Using a Kaggle Playground data to implement ML and DL techniques and further drawing comparisons.

Here you'll find the required dependencies, structures, implementation for individual Algorithms. Have fun!

Classical ML&DL algorithms' implementations