amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

animated-linked-list

Describing the working of linked list in data structure by the help of graphics in C-language.

audio-track-separation

In this Repository, We developed an audio track separator in tensorflow that successfully separates Vocals and Drums from an input audio song track.

cyclegan-with-self-attention

In this repository, I have developed a CycleGAN architecture with embedded Self-Attention Layers, that could solve three different complex tasks. Here the same principle Neural Network architecture has been used to solve the three different task. Although truth be told, my model has not exceeded any state of the art performances for the given task, but the architecture was powerful enough to understand the task that has been given to solve and produce considerably good results.

data-visualiaztion-on-temperature-change-of-a-region

Data Visualiaztion on temperature change of a region in a particular range of time comparing the maximum recorded temperature and minimum recorded temperature of that region

ddqn-with-pytorch-for-openai-gym

Implementation of Double DQN reinforcement learning for OpenAI Gym environments with PyTorch.

e-commerce-website

Developing an e-commerce website with HTML, CSS, js, bootstrap and PHP.

end2endautomaticspeechrecognition

In this repository, I have developed an end to end Automatic speech recognition project. I have developed the neural network model for automatic speech recognition with PyTorch and used MLflow to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry.

exoplanet-hunting-in-deep-space

Exoplanet hunting in deep space with the help of gradient boosting algorithm

facial-emotion-recognition

This repository demonstrates an end-to-end pipeline for real-time Facial emotion recognition application through full-stack development. The frontend is developed in react.js and the backend is developed in FastAPI. The emotion prediction model is built with Tensorflow Keras, and for real-time face detection with animation on the frontend, Tensorflow.js have been used.

googlesheetplot

This library helps a user to select a google sheet from their Google drive and plots a chart with the values on the sheet. The user only needs to select the column for the x-axis and the y-axis.

hypothesis-testing

Hypothesis testing of housing prices due of recession in university and non university towns

intelligent-alarm-clock-with-todo-list

An intellegent GUI alarmclock integrated with todo list in python progamming language

keras-io

Keras documentation, hosted live at keras.io

lime-for-time

Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification

mongodbflask

This repository explains how to create a REST API using Python and host it locally using Docker. The goal of this task is to allow the user to interact with a database of products using APIs which are available on localhost via Docker. REST API has been created with the help of flask, that allows the user to do basic CRUD operations on the data.

myosuiteddqn

In this repository, we try to solve musculoskeletal tasks with `Double DQN reinforcement learning` by using a `transformer` model has been used as the base model architecture.

predict-the-energy-used

predict the electricity usage of heating and cooling appliances in a household based on internal and external temperatures and other weather conditions. This machine learning model have aquired an accuracy of 0.59454 and a rank of 73 in "Machine Learning challenge #5" organised by "Hackerearth".

prediction-of-asteroid-diameter-with-mlp

Prediction of diameter of asteroids with Multi-Layered Perceptrons

proteinstructureprediction

Protein structure prediction is the task of predicting the 3-dimensional structure (shape) of a protein given its amino acid sequence and any available supporting information. In this section, we will Install and inspect sidechainnet, a dataset with tools for predicting and inspecting protein structures, complete two simplified implementations of Attention based Networks for predicting protein angles from amino acid sequences, and visualize our predictions along the way.

rasachatbot

Developing a chatbot assistant using RASA. The chatbot is built to handle basic hotel chat functionlities like Book room, Request Room Cleaning, Handle FAQs, Handle Greetings

real-time-stock-market-prediction

In this repository, I have developed the entire server-side principal architecture for real-time stock market prediction with Machine Learning. I have used Tensorflow.js for constructing ml model architecture, and Kafka for real-time data streaming and pipelining.

relationship-between-model-complexity-and-generalized-performance

Exploring the relationship between model complexity and generalization performance, by adjusting key parameters of various supervised learning models. Part 1 of this assignment will look at regression and Part 2 will look at classification.

respiratory-diseases-recognition-through-respiratory-sound-with-the-help-of-deep-neural-network

Prediction of respiratory diseases such as COPD(Chronic obstructive pulmonary disease), URTI(upper respiratory tract infection), Bronchiectasis, Pneumonia, Bronchiolitis with the help of deep neural networks or deep learning. We have constructed a deep neural network model that takes in respiratory sound as input and classifies the condition of its respiratory system. It not only classifies among the above-mentioned disease but also classifies if a person’s respiratory system is healthy or not with higher accuracy and precision.

sagemakerhuggingfacedashboard

This is a solution that demonstrates how to train and deploy a pre-trained Huggingface model on AWS SageMaker and publish an AWS QuickSight Dashboard that visualizes the model performance over the validation dataset and Exploratory Data Analysis for the pre-processed training dataset.

stack-realistic

realistic static stack using pointers

tensorflow-in-sagemaker

tic-tac-toe

A graphics based tic tac toe game in C- language

titaanic-survival-prediction

Machine Learning model with an accuracy of 0.76076 and a rank of 6423 in "Kaggle's Titanic survival prediction competition"

understanding-and-predicting-property-maintenance-fines

The Michigan Data Science Team (MDST) and the Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS) have partnered with the City of Detroit to help solve one of the most pressing problems facing Detroit - blight. Blight violations are issued by the city to individuals who allow their properties to remain in a deteriorated condition. Every year, the city of Detroit issues millions of dollars in fines to residents and every year, many of these fines remain unpaid. Enforcing unpaid blight fines is a costly and tedious process, so the city wants to know: how can we increase blight ticket compliance?.The first step in answering this question is understanding when and why a resident might fail to comply with a blight ticket. This is where predictive modeling comes in.task is to predict whether a given blight ticket will be paid on time.