Category: AI/Machine Learning

https://www.furygpu.com/blog/hello   https://eater.net/8bit https://digilent.com/shop/arty-z7-zynq-7000-soc-development-board/   https://www.amd.com/en/products/system-on-modules/kria.html  

Docker image: https://hub.docker.com/r/siutin/stable-diffusion-webui-docker docker pull siutin/stable-diffusion-webui-docker:latest-cpu   https://github.com/siutin/stable-diffusion-webui-docker Run it: docker run -it –name sdw –network host -v $(pwd)/models:/app/stable-diffusion-webui/models -v $(pwd)/outputs:/app/stable-diffusion-webui/outputs –rm siutin/stable-diffusion-webui-docker:latest-cpu bash webui.sh –skip-torch-cuda-test –no-half –use-cpu all –share…

(1) use container https://github.com/abetlen/llama-cpp-python/pkgs/container/llama-cpp-python (2) mount k8s storage as /models export MODEL point to the right llama-model.gguf (3) expose 8000 to loadbalancer to outside (4) browse to ip:8000/docs for API…

Ollama is a front-end written in go, and wrap-up the back-end of llama.cpp Here is the steps for setup ollama in k8s cluster (1) write a k8s yaml file, where…

HW Purpose: Add old laptop  to existing k8s HW: Add Laptop with only Wifi ( no ethernet port) Software debian 12 (1) k8s apt-get install bridge-utils apt-get install nfs-common sudo…

ML course from Google https://developers.google.com/machine-learning   Probabilistic Machine Learning: An Introduction https://probml.github.io/pml-book/book1.html Probabilistic Machine Learning: Advanced Topics https://probml.github.io/pml-book/book2.html

https://developers.google.com/machine-learning/testing-debugging/metrics/interpretic A very useful guide

Basic knowledge https://developers.google.com/machine-learning/crash-course/embeddings/video-lecture A good one : explain embedding and recommendation system Type Definition Example content-based filtering Uses similarity between items to recommend items similar to what the user likes. If user…

Handling imbalanced datasets is a common challenge in machine learning, especially for classification tasks where the number of examples in different classes varies significantly. Imbalanced datasets can lead to biased…

Evaluating the performance of machine learning models is a crucial step to understand how well your model is generalizing to new, unseen data. There are various evaluation metrics and techniques…

KNN (K-Nearest Neighbors), K-means, and Mean Shift are all machine learning techniques used for different types of tasks, primarily in clustering and pattern recognition. Let’s break down the differences between…

Boosting and bagging are both ensemble learning techniques in machine learning that aim to improve the performance of individual models by combining the predictions of multiple base models. They work…