Name: Somsirsa Chatterjee
Type: User
Company: Chatterjee Foundation
Bio: Head - Quality Enhancement and Compliance Task Force (QECTF), Founder - @Chatterjee-Foundation, Data Science Expert, AI specialist, Masters, Biomedical Engineer
Location: India
Blog: http://chatterjeefoundation.in
Somsirsa Chatterjee's Projects
My Answer to 120 Data Science Interview Questions
Methodology and code supporting the BuzzFeed News/BBC article, "The Tennis Racket," published Jan. 17, 2016.
📝 Easily create a beautiful website using Academic, Hugo, and Netlify
The EFF Action Center Platform
Fast and flexible AutoML with learning guarantees.
Pseudocode descriptions of the algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
Interpretability and explainability of data and machine learning models
A simple Flutter project to cover basic training.
Multiple samples showing the best practices in app bundles on Android.
Learn applied deep learning from zero to deployment using TensorFlow 1.8+
Code Repository for Applied Deep Learning with TensorFlow and Google Cloud AI, Published by Packt
Apps Script samples for G Suite products.
Code repository for Artificial Intelligence with Python, published by Packt
Official repository for the Automatic Tinder Tutorial
A curated list of awesome machine learning interpretability resources.
A Python SDK for connecting devices to Microsoft Azure IoT services
Batch Renormalization algorithm implementation in Keras
a general purpose learning agent
TensorFlow code and pre-trained models for BERT
Free, feature-rich, easily customizable Android dashboard for icon packs
Hide screen when boss is approaching.
BR41N.IO Hackathon repository
Python package for causal inference using Bayesian structural time-series models.
Python port of CausalImpact R library
Visualization toolkit for Dart language
The ChatGPT Retrieval Plugin lets you easily search and find personal or work documents by asking questions in everyday language.
Using cifar-10 datasets to learn deep learning.
Predict CIFAR-10 labels with 88% accuracy using keras.