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Understanding Deep Learning - Simon J.D. Prince
Eclipse iceoryx™ - true zero-copy inter-process-communication
slamplay is a collection of powerful tools to start playing and experimenting with SLAM in C++
[ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
Count the MACs / FLOPs of your PyTorch model.
Image Signal Processing (ISP) Guide. Learn all about the process of converting an image/video into digital form by performing tasks like noise reduction, filtering, auto exposure, autofocus, HDR co…
Visualize, query, and stream to train on multimodal robotics data.
A curated list of radar datasets, detection, tracking and fusion
Python sample codes and textbook for robotics algorithms.
The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++
🤖 Places where you can learn robotics (and stuff like that) online 🤖
A curated list of awesome embedded and IoT security resources.
Solving the Traveling Salesman Problem using Self-Organizing Maps
Painless E2E Automation for Mobile and Web
A curated list of awesome embedded programming.
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
This repository contains material related to A Versatile Cybersecurity Development Lifecycle (AVCDL)
Delta-sigma audio DAC (16b, 48kHz), intended for tape-out on MPW-5, SKY130 technology.
A curated list of awesome 3d generation papers
Reproducing Yann LeCun 1989 paper "Backpropagation Applied to Handwritten Zip Code Recognition", to my knowledge the earliest real-world application of a neural net trained with backpropagation.
Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
Collection of tech talks, papers and web links on Distributed Systems, Scalability and System Design.
List of Computer Science courses with video lectures.
🟠 A study guide to learn about Graph Neural Networks (GNNs)
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.