#other #deep_learning #graph #graph_algorithms #awesome_list #graphml #explainable_ai #explainable_ml

https://github.com/AstraZeneca/awesome-explainable-graph-reasoning

#common_lisp #art #common_lisp #generative #generative_art #graph #graph_algorithms #lisp #plotter_art #plotters #svg #vector_graphics

https://github.com/inconvergent/weird

#go #graph #graph_algorithms #graph_theory #graph_traversal #graphs

https://github.com/dominikbraun/graph

#rust #client_server #cozo #cozoscript #cross_platform #database #datalog #embedded_database #graph #graph_algorithms #graph_database #graphdb #relational_database #single_executable

https://github.com/cozodb/cozo

#cplusplus #caffe #convolution #deep_learning #deep_neural_networks #diy #graph_algorithms #inference #inference_engine #maxpooling #ncnn #pnnx #pytorch #relu #resnet #sigmoid #yolo #yolov5

This course, "_动手自制大模型推理框架_" (Handcrafting Large Model Inference Framework), is a valuable resource for those interested in deep learning and model inference. It teaches you how to build a modern C++ project from scratch, focusing on designing and implementing a deep learning inference framework. The course supports latest models like LLama3.2 and Qwen2.5, and uses CUDA acceleration and Int8 quantization for better performance.

By taking this course, you will learn how to write efficient C++ code, manage projects with CMake and Git, design computational graphs, implement common operators like convolution and pooling, and optimize them for speed. This knowledge will be highly beneficial for job interviews and advancing your skills in deep learning. The course also includes practical demos on models like Unet and YoloV5, making it a hands-on learning experience.

https://github.com/zjhellofss/KuiperInfer