Ritabrata Maiti's repositories
Chem-Faiss
This projects utilises vector similarity search from Faiss, in conjunction with chemical fingerprinting to build a scalabale similarity search architecture for compounds/molecules.
ASD-ML-API
This project has 3 goals: To find out the best machine learning pipeline for predicting ASD cases using genetic algorithms, via the TPOT library. (Classification Problem) Compare the accuracy of the accuracy of the determined pipeline, with a standard Naive-Bayes classifier. Saving the classifier as an external file, and use this file in a Flask API to make predictions in the cloud.
pyRunBrowser
Code Editor + Browser Runtime for Python
Blooddonorprediction
Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage of classifiers. In this study, we look at a Blood Transfusion Service Center Data Set (Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python. From Support Vector Machines(SVM), we use Support Vector Classification(SVC), from the linear model we import Perceptron. We also used the K.neighborsclassifier and the decision tree classifiers. We segmented the database into the 2 parts. Using the first, we trained the classifiers and the next part was used to verify if the classifier prediction matched that of the actual values.
DL4GP
DL4GP(Deep Learning for Genomics and Proteonomics) is a project which leverages Deep Learning to learn patterns from genetic and protein sequence. We propose neural network architectures which can perform variety of tasks using NLP like techniques such as identifying sequences and drug discovery.
generated-drones
Drone designs generated with genetic algorithms
AI_in_Finance
This is the code for "AI in Finance" By Siraj Raval on Youtube
chucknorrisjokes
Chuck Norris Jokes Generator (JS API Project)
covid19-dashboard
🦠Django + Plotly Coronavirus dashboard. Powerful data driven Python web-app, with an awesome UI. Contributions welcomed! Found on 🕶Awesome-list
Daily-Journal
JSON files mostly
faiss
A library for efficient similarity search and clustering of dense vectors.
flask-hello-world
Flask Hello World Example for Render
NeuralTuringMachine
Tensorflow implementation of a Neural Turing Machine
publications
My Publications
python-packaging
Tutorial on how to structure Python packages
quart-schema
Quart-Schema is a Quart extension that provides schema validation and auto-generated API documentation.
scikit-learn
scikit-learn: machine learning in Python
startbootstrap-new-age
A web app landing page theme created by Start Bootstrap
stockraptor
Stock Raptor combines an Artificial Intelligence powered dashboard with a stock ticker. Stock Raptor is currently in an open beta with free access!
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.