There are 26 repositories under end-to-end-machine-learning topic.
Convert images of LaTex math equations into LaTex code.
[ECCV2022, TPAMI2023] FAST-VQA, and its extended version FasterVQA.
Grounded conversational dataset for end-to-end conversational AI (official DSTC7 data)
In this project, the objective is to predict whether the person has Diabetes or not based on various features like Number of Pregnancies, Insulin Level, Age, BMI.
PyTorch codebase for zero-shot dialog generation SIGDIAL 2018, It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU
Autonomous Literature Overview
An end-to-end video restoration project with open-source pretrained deep learning models
Companion repository for the paper "A Comparison of Metric Learning Loss Functions for End-to-End Speaker Verification" published at SLSP 2020
ROS2 End-to-End Lane Following Model with SVL Simulator
End-to-end image search engine based on the Deep learning techniques.
End-to-end robot control based on generative diffusion model
A web-app to identify toxic comments in a youtube channel and delete them.
End to end machine leanring project: This repository serves as a simplified guide to help you grasp the fundamentals of MLOps.
Recognize handwritten multi-digit numbers using a CRNN model trained with synthetic data.
Machine Learning Stack for Big Data, Big Cluster and Big Challenges
This is the main repository for the Book Search project. This engine allows you to search database of books by simply uploading an image of a cover.
Using machine learning algorithms, this project aims to predict student performance in standardized tests based on demographic and academic data.
An end-to-end machine learning project, student performance indicator. The goal of this project is to understand the influence of the parents background, test preparation, and various other variables on the students performance.
Bare minimum End-to-End ML application with Flask REST API Prediction Service
A corpus that can be used to train English-to-Italian End-to-End Speech-to-Text Machine Translation models
Cardio Monitor is a web app that helps you to find out whether you are at risk of developing heart disease. the model used for prediction has an accuracy of 92%. This is the course project of subject Big Data Analytics (BCSE0158).
This is a Liver Disease Machine Learning Classification Capstone Project in fulfillment of the Udacity Azure ML Nanodegree. In this project, you will learn to deploy a machine learning model from scratch. The files and documentation with experiment instructions needed for replicating the project, is provided for you.
Build a serverless deep learning model (end-to-end implementation) to classify real-world clothes images using TensorFlow, TensorFlow Lite, Docker, and AWS Lambda with API Gateway.
This repository has code of how to train a RNN that can perform overtaking in F1TENTH simulator as well as a dataset I have created
Sentiment Analysis WebApp using LSTM, made with :heart: in PyTorch and deployed on AWS:rocket: Do :star2: the repo and show some love.
Self-Driving Car Behavioral Cloning based End-to-End learning, Computer Vision & Deep Neural Network
This repository contains the code to replicate the End-to-End Deep Sequence Modelling baseline for the Breathing Challenge of the Interspeech 2020 Computational Paralinguistics Challenge (ComParE).
A Deep Neural Network Plays Crew 2
A simple deep learning library for training end-to-end fully-connected Artificial Neural Networks (ANNs), primarily based on numpy and autograd.
Explore a modular, end-to-end solution for heart disease prediction in this repository. From problem definition to model evaluation, dive into detailed exploratory data analysis. Experience seamless integration with MLOps tools like DVC, MLflow, and Docker for enhanced workflow and reproducibility.
PyTorch implementation of "Exploring End-to-end Differentiable Neural Charged Particle Tracking -- A Loss Landscape Perspective".
An end-to-end content-based TMDB movie recommendation engine developed using PySpark, Flask, and Angular.
Hate speech detection using a Decision Tree algorithm classifies text as hateful or non-hateful by evaluating features such as words and phrases. The algorithm builds a tree of decision rules, splitting nodes based on feature values, to predict the category of text, aiding in identifying and moderating harmful content.