Best learning resources for AI
Xorbits’ universal inference layer (library name `xinference`) that deploys and serves LLMs and multimodal models from laptop to cluster.
LiteLLM is an open-source LLM gateway and Python SDK that lets developers call more than 100 commercial and open-source models through a single OpenAI-compatible interface, complete with cost tracking, rate-limiting, load-balancing and guardrails.
Self-hosted, extensible AI chat platform and Ollama/OpenAI interface.
fast-agent lets you define, prompt and test sophisticated agents and workflows with full MCP support in minutes.
NVIDIA’s open-source library that compiles Transformer blocks into highly-optimized TensorRT engines for blazing-fast LLM inference on NVIDIA GPUs.
The book introduces core principles and theoretical foundations behind deep learning, bridging the gap between classical machine learning and modern neural networks. It explains key architectures, optimization techniques, and mathematical frameworks that underpin today’s AI systems. By combining rigorous treatment with accessible explanations, it empowers researchers and practitioners to understand not just how deep models work, but why. Its impact lies in deepening the academic rigor of the field, shaping curricula, and guiding both industry innovation and the next generation of AI breakthroughs.
CUDA kernel library that brings Flash-attention-style optimizations to any LLM serving stack.
High-performance Python framework and platform for orchestrating collaborative agent “crews”.
FastGPT is an open-source AI knowledge-base platform that combines RAG retrieval, visual workflows and multi-model support to build domain-specific chatbots quickly.
The book offers a clear, intuitive introduction to deep learning, breaking down complex mathematical ideas into accessible explanations with vivid illustrations. It covers essential topics like neural networks, backpropagation, optimization, and modern architectures, making it ideal for newcomers and practitioners seeking conceptual clarity. Its impact lies in demystifying deep learning’s core principles, empowering a broad audience to engage with cutting-edge machine learning research and applications, and serving as a valuable bridge between foundational theory and practical implementation in the rapidly evolving AI landscape.
Zero-code CLI & WebUI to fine-tune 100+ LLMs/VLMs with LoRA, QLoRA, PPO, DPO and more.
Microsoft Research approach that enriches RAG with knowledge-graph structure and community summaries.
