AI 教程

Learn how to use the DeepSeek API, including model selection, parameter management, cost optimization, context caching, and chain-of-thought optimization.

Learn the step-by-step process of setting up a RAG application using Llama 3.2 Vision, Ollama, and ColPali.

Deploy the AWQ version of Llama 3.3 70B using vLLM, BentoML, and BentoCloud to create a highly efficient, low-latency system with exceptional throughput.

Learn the hill climbing algorithm in Python. This guide covers types, limitations, and real-world AI applications with code examples.

Learn how to build and publish a mobile app using Lovable AI, integrating it with Supabase for backend services and GitHub for version control.

Learn about the new flagship vision-language model and run it on a laptop with 8GB VRAM.

Learn to code an unbeatable Tic-tac-toe AI using the Minimax algorithm in Python. This tutorial covers theory, implementation, and optimization, ideal for game AI enthusiasts.

Learn how to install, set up, and run DeepSeek-R1 locally with Ollama and build a simple RAG application.

In this DeepSeek-R1 tutorial, you'll learn how to build a math puzzle solver app by integrating DeepSeek-R1 with EasyOCR and Gradio.

A beginner-friendly guide to TorchRL for deep reinforcement learning—learn to build RL agents with PyTorch through practical examples.

Learn how to set up the DeepSeek Janus-Pro project, build your own Docker image, and run the Janus web application locally on your laptop.

Fine-tuning the world's first open-source reasoning model on the medical chain of thought dataset to build better AI doctors for the future.