Deeplearning (pytorch, transformers, GANs, BERT, RNNs)

This is a repository of my Google Colab notebooks for various machine/deep learning problems.

Backup for kaggle.com notebooks

セマンティックセグメンテーションを実装するためのデモ

Notebooks to segment wave contour using RunPod

Scripts & notebooks from the beggining of my Ph.D.

Unets notebook developed while I was working in NUS for multiclass 3D segmentation

In this project, I will analyze a dataset containing data on various customers' annual spending amounts (reported in monetary units) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip…

Este repositorio presenta una implementación de segmentación de mamas utilizando YOLOv8. La segmentación de mamas es crucial en el diagnóstico de cáncer de mama. El proyecto incluye un notebook detallado y una demostración desplegada para probar el modelo en tiempo real.

Wound healing analysis and segment the wound area

A Python-based simulator showcasing memory management concepts such as allocation strategies, segmentation, pagination, swapping, and TLB. Interactive examples implemented in Jupyter notebooks

Jupyter Notebooks e codigos para analise de bioimagens e dados biologicos utilizados no meu canal BioPrograma - https://www.youtube.com/channel/UCbJAU7N9FYvwkdgSwD_1S4Q

This repository utilizes YOLOv8 for object detection and segmentation in images and videos. It does not include any training or fine-tuning; instead, it uses pre-trained models. The implementation is provided in two ways: via command-line execution and Python scripting. To run and use YOLOv8, simply execute the notebook in Google Colab.

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Repository for the Capstone Project of the MBD-PT January 2022. This repository contains several notebooks performing detailed analysis of the data, identification of Market Drivers, segmentation of stores, predictions of future sales and design of appropriate promotional strategies to extract the most value upon Beer sales in Spain

Notebooks of pre trained models using the HAM10000 dataset

Computer Vision Paper Reviews with Key Summary of paper, End to End Code Practice and Jupyter Notebook converted papers