deep

YoloV3 in Pytorch and Jupyter Notebook

Jupyter Notebook tutorials for ECE 046211 Deep Learning course at the Technion

A deep learning and image processing project used to predict the emotions of a person in image.

Codes, scripts, and notebooks on various aspects of transformer models.

This repository contains Jupyter Notebooks showcasing different techniques for price prediction using Artificial Neural Networks (ANNs). Topics covered include early stopping, batch normalization, dropout regularization, and deep neural networks. These notebooks offer practical implementations and insights for improving model training

Pytorch Tutorial Notebooks

Implementation of Everything in the video lectures of Machine Learning A-Z course on Udemy by Kirill Eremenko and Hadelin de ponteves in jupyter notebook

Machine Learning Model to detect hidden malwares and phase changing malwares.It predicts the date of the next probable attack of the malware and its extent.It deals with the change in network traffic flow.It is developed in Python in Jupyter notebook.

Predicting NBA MVPs using machine learning

Interactive chatbot for nishauri

Python jupyter notebook project on learning and detecting traffic signs

This repository consists of notebook, backtesting logs and dataset along with the Problem Statement. This is was our approach to KDSH 2024 by Zelts Labs

Template Repo for Deep / Machine Learning Projects

A Machine Learning research on Deep Learning-based models like Decision Trees, Neural Networks, KNN using pure-Python tools like pandas, numpy, matplot, sklearn, tensorflow, kareas and Jupyter Notebook

Code for the Master's thesis on post-disaster building damage detection in Palu (2018) using the YOLOSeg11 model with xView2 training data and WorldView-2 imagery. Includes notebooks for training, inference, and evaluation.

A comprehensive suite of Computer Vision implementations ranging from low-level pixel manipulation to high-level object detection and deep learning classification. This repository focuses on real-world utility, featuring production-ready notebooks that utilize OpenCV, TensorFlow, and Keras to solve complex visual problems.

This project explores the geometry and probabilistic structure of deep neural network feature spaces, with a focus on class separability, representation collapse, and robustness under adversarial perturbations. Using pretrained models (e.g., ResNet variants) and datasets such as CIFAR-100, the repository provides analysis notebooks