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Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Stanford CS231n Convolutional Neural Networks for Visual Recognition Assignments
VIP cheatsheets for Stanford's CS 229 Machine Learning
CS229 course notes from Stanford University on machine learning, covering lectures, and fundamental concepts and algorithms. A comprehensive resource for students and anyone interested in machine l…
PyTorch Implementation of "Resource Efficient 3D Convolutional Neural Networks", codes and pretrained models.
The correct way to resize images or tensors. For Numpy or Pytorch (differentiable).
A playbook for systematically maximizing the performance of deep learning models.
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
🐍 Geometric Computer Vision Library for Spatial AI
Very fast greedy diffeomorphic registration code
LST-AI - Deep Learning Ensemble for Accurate MS Lesion Segmentation
A collection of my book notes on various subjects, mainly computer science
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
Lists of company wise questions available on leetcode premium. Every csv file in the companies directory corresponds to a list of questions on leetcode for a specific company based on the leetcode …
Writing AI Conference Papers: A Handbook for Beginners
A generative world for general-purpose robotics & embodied AI learning.
From scratch implementation of a vision language model in pure PyTorch
The SILE Typesetter — Simon’s Improved Layout Engine
A new markup-based typesetting system that is powerful and easy to learn.
🧙 Build, run, and manage data pipelines for integrating and transforming data.
Make awesome display tables using Python
Lightweight and extensible compatibility layer between dataframe libraries!
ETL, Analytics, Versioning for Unstructured Data
Lightning-fast serving engine for any AI model of any size. Flexible. Easy. Enterprise-scale.
A reactive notebook for Python — run reproducible experiments, execute as a script, deploy as an app, and version with git.
WebApps in pure Python. No JavaScript, HTML and CSS needed