TensorFlow

Project evaluates PyTorch and TensorFlow matrix multiplication performance on macOS.

This project aims to build a Celebrity Look-A-like Model using CNN (Convolutional Neural Networks) based on the VGG (Visual Geometry Group) 16 Model. Users can enjoy a lighthearted exploration of their celebrity doppelgängers. The project integrates image processing, CNN implementation, transfer learning and practical neural network applications, aligning with the course's focus on the practical implementation of Deep Neural Networks.

This project focuses on building a Lyric Classification Model using LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Networks). The models' purpose is to identify which artist a given lyric belongs to. Users can input lyrics and the models will predict the associated artist, aligning with the course's focus on the practical implementation of Natural Language Processing (NLP) tasks.

The project focuses on developing a predictive tool for chess using recurrent neural network (RNN) models implemented in TensorFlow. Data collection was carried out using web scraping techniques from the website https://www.chess-poster.com.

El proyecto se orienta hacia el desarrollo de una herramienta predictiva para el ajedrez, empleando modelos de red neuronal recurrente (RNN) implementados en TensorFlow. La recopilación de datos se realizó mediante técnicas de web scraping desde la página https://www.chess-poster.com.

This project builds a Lyric Generation Model using LSTM and CNN neural networks to create song lyrics across different genres. Focusing on NLP tasks, the model will be trained on a large corpus of song lyrics. To ensure portability and ease of deployment, the project will be Dockerized, allowing users to run the model seamlessly across different environments without setup issues. Docker will package all dependencies, providing a consistent workflow for development, testing, and production.

An example Python project for simulating federated learning using federated averaging.

This project aims to leverage the Ames Housing Price Dataset to develop an accurate predictive model for estimating residential property sale prices in Ames, Iowa. By analyzing various property features and their relationships to sale prices, the project seeks to uncover insights that can inform both buyers and sellers in the housing market.

A Python library for Secure and Explainable Machine Learning

Documentation available @ https://secml.gitlab.io

Follow us on Twitter @ https://twitter.com/secml_py

бинарный предобученный классификатор со всеми метриками и предсказанием

MediScanAI is a computer vision healthcare API project applying AI for medical insights. It classifies brain tumor types based on tomography images.

Introduction to classification using machine learning and deep learning (PyTorch, TensorFlow, Keras)

Graduate Tools and Models - Data Science.

Rice University, Spring 2024.

This project provides a deep learning approach to learn machining features from CAD models using a hierarchical graph convolutional neural network.

ICT deep learning lab

A project focused on weather classification using advanced deep learning techniques, specifically leveraging TensorFlow and a custom Convolutional Neural Network (CNN). The project involved the integration of four diverse weather datasets, namely ACDC, MWD, UAVid, and Syndrone, covering various weather conditions, including clear sky, cloudy, rainy, and sunny weather. Developed a custom CNN architecture using TensorFlow's Keras API, incorporating convolutional layers for feature extraction and dense layers for classification.

This Car Prediction Project aims to predict v price, using Regression models. The project contains a collection of data files, model files, and Python scripts necessary for training and deploying car prediction Price models. This project encompasses a comprehensive set of data files, serialized models, and Python scripts necessary for training, evaluating, and deploying car price prediction models.

Mobile application that aims to classify waste into two, namely organic waste and inorganic waste. From the results of this classification, waste can be sold to the nearest garbage collector or junkyard according to the type of waste.

An attempt at AI-based lemmatization (or something close to lemmatization) of Old English