Data Science Tutorials

Self-supervised learning trains models to pull their own supervision from unlabeled text, images, audio, and video, using techniques like contrastive learning, masked modeling, and autoregressive prediction to build the representations behind today's language, vision, and multimodal models.

Set up Muse Spark 1.3 in Muse Code and the Meta Model API, choose between the contributor and standard tiers, and see whether Meta's efficiency claims hold.

Learn how to use the Gemini 3.8 Flash API in Python: Interactions API setup, thinking_level tuning, PDF-to-JSON extraction, and function calling with code.

Use GPT-6 Astra via the OpenAI API to build a Python release check agent with async tools, reasoning controls, Structured Outputs, and cost tracking, then test computer use and steering.

Learn how to build a custom AI agent harness with Python, ReAct loops, and durable state.

Learn how to use Anthropic’s latest flagship model to build a Python agent that reads a Flask repository before planning a change. Add progress updates, read-only file tools, and cost controls.

Use Unsloth Desktop for local AI workflows, including LLM inference, fine-tuning, vision models, image and video generation, OpenCode coding agents, and local APIs from one desktop application.

Learn how quantization, KV-cache optimization, batching, speculative decoding, memory bandwidth, and hardware-aware tuning can improve speed, scalability, and cost efficiency when serving large language models.

Use GLM-5.3-Flash and CrewAI to build 3 specialist LLM agents covering stock fundamentals, technicals, and news, and merge their findings into a BUY/SELL signal.

Learn how to run Qwen3.8-Flash-Next GGUF locally with llama.cpp on an RTX PRO 6000, then connect it to OpenCode for a fully local agentic coding setup.

Learn how Grok Bot's Bots, shared cloud computer, skills, and routines fit together by building one agent that researches, verifies, and reports every Monday.

Learn how to run Qwen3.8-27B locally with Blackwell-native NVFP4 and MTP speculative decoding, achieving up to 170 tokens per second with llama.cpp.