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The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Python Data Science Handbook: full text in Jupyter Notebooks
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Neural Networks: Zero to Hero
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 filte…
A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
High-Resolution Image Synthesis with Latent Diffusion Models
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Think DSP: Digital Signal Processing in Python, by Allen B. Downey.
This is Andrew NG Coursera Handwritten Notes.
Official codebase used to develop Vision Transformer, SigLIP, MLP-Mixer, LiT and more.
An educational AI robot based on NVIDIA Jetson Nano.
Theory of digital signal processing (DSP): signals, filtration (IIR, FIR, CIC, MAF), transforms (FFT, DFT, Hilbert, Z-transform) etc.
Tutorials on how to use pandas effectively to do data analysis
Pix2Seq codebase: multi-tasks with generative modeling (autoregressive and diffusion)
Graph Machine Learning course, Xavier Bresson, 2023
18.335 - Introduction to Numerical Methods course
Parallel Hyperparameter Tuning in Python
Tutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]
Notes and plans for fastdiffusion course
Materials for EECS 445, an undergraduate Machine Learning course taught at the University of Michigan, Ann Arbor.
This is the code for "C Programming for Machine Learning" By Siraj Raval on Youtube
MLDA Workshop Materials for AY2021-22
Winning 2nd place🥈at NUS CS5228 in-class Kaggle competition 2018!