Учебные материалы по ИИ

A practical overview of contrastive learning - how models learn by comparing similar and dissimilar examples, the loss functions and methods behind it, and how to implement it in PyTorch.

Learn how to use conversational analytics, BigQuery Studio's newest Gemini-powered feature, to build intelligent data agents with natural language queries.

Use the Cursor SDK to run a local agent, then run a cloud agent that fixes a small bug in a GitHub repo and opens a pull request.

Learn how DeepSeek V4's three reasoning modes work, and build a Streamlit comparison app that shows when each mode actually wins on quality, speed, and cost.

Learn to fine-tune NVIDIA Nemotron-3-Nano-4B on a psychology Q&A dataset using an RTX 3090 GPU using LoRA and TRL after downloading the model from Hugging Face.

Learn how to build an OpenAI agent app that runs inside Modal Sandboxes, works with files, executes code, and returns results in this hands-on Python tutorial.

Learn how to use Alibaba's Qwen 3.6 Plus, Python, and the OpenAI SDK to build an automated invoice processing pipeline with native vision and tool calling.

Build a Streamlit benchmark application that tests whether Opus 4.7 self-critique memory actually improves coding performance across high, xhigh, and max effort levels.

Learn how to pair Claude Code Auto Mode with Telegram Channels to run an autonomous write-test-debug loop from your phone.

Learn how to fine-tune Qwen3.6 on an H100 NVL GPU using SFT, from dataset preparation and 4-bit loading to training and evaluation.

Learn how to build an automated LLM-as-a-judge system to evaluate your RAG pipelines for faithfulness and relevance at scale and bridge the gap in AI testing.

Learn the capabilities of Anthropic’s best publicly available model, Claude Opus 4.7, and build a data science tool that can turn a chart into raw data.