This is my personal webpage: http://shashirajpandey.github.io
shashirajpandey Goto Github PK
Type: User
Bio: Computer Science and Engineering.
Twitter: shashirajpandey
Location: Nepal
Type: User
Bio: Computer Science and Engineering.
Twitter: shashirajpandey
Location: Nepal
This is my personal webpage: http://shashirajpandey.github.io
🧑🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
Reinforcement learning based scheduling algorithm for optimizing AoI in URLLC networks
Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web
Lightweight, useful implementation of conformal prediction on real data. (a.k.a. conformal inference)
Dive into Deep Learning: an interactive deep learning book on Jupyter notebooks, using the NumPy interface.
Cheat Sheets
Introduction to Deep Neural Networks with Keras and Tensorflow
An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.
Democratized Learning
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
Official code for "Federated Multi-Task Learning under a Mixture of Distributions" (NeurIPS'21)
Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
code to reproduce the empirical results in the research paper
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Leaf: A Benchmark for Federated Settings
Classical equations and diagrams in machine learning
Simulate a federated setting and run differentially private federated learning.
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Neural Networks: Zero to Hero
Numerical Tours of Signal Processing
Federated Learning over Wireless Networks
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Python code for "Machine learning: a probabilistic perspective" (2nd edition)
All Algorithms implemented in Python
Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
Deep Learning (with PyTorch)
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.