#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

#cplusplus #arm #convolution #deep_learning #embedded_devices #llm #machine_learning #ml #mnn #transformer #vulkan #winograd_algorithm

MNN is a lightweight and efficient deep learning framework that helps run AI models on mobile devices and other small devices. It supports many types of AI models and can handle tasks like image recognition and language processing quickly and locally on your device. This means you can use AI features without needing to send data to the cloud, which improves privacy and speed. MNN is used in many apps, including those from Alibaba, and supports various platforms like Android and iOS. It also helps reduce the size of AI models, making them faster and more efficient.

https://github.com/alibaba/MNN