Discover the Best AI Resources
Curated essentials, no noise — just what matters
MLOps2018
Orchestrates and schedules Python data pipelines and workflows with primitives for retries, caching, parameters, and deployments. Provides either a self-hosted server or managed Prefect Cloud for monitoring, observability, and integrations across common data tools.
Condenses Stanford's CS 229 into one-page visual cheatsheets spanning supervised, unsupervised, and deep learning, plus probability and linear-algebra refreshers. Available in 10+ languages, with all topics merged into one Super VIP PDF.
AI Image2018
Brings classic computer vision into PyTorch as differentiable, GPU-accelerated tensor operators — filters, geometric transforms, feature matching, camera calibration — so each step lives inside autograd and trains end-to-end with neural networks.
AI Deploy2018
Serves machine learning and deep learning models for cloud, data center, edge and embedded environments. Supports multiple frameworks and backends, dynamic and sequence batching, HTTP/gRPC APIs, Docker deployment and NVIDIA-optimized runtimes.
Notebook-first deep learning textbook that teaches concepts through runnable multi-framework code, math, and exercises. Includes lecture-ready notebooks, community contributions, and broad university adoption—designed for hands-on learners and instructors.
Pre-trains a deep bidirectional Transformer encoder with masked-language-modeling and next-sentence prediction, then fine-tunes one model on 11 NLP tasks, reaching state-of-the-art on GLUE, SQuAD, and MultiNLI with little task-specific tuning.
AI Deploy2018
Converts, quantizes, and runs deep learning models from PyTorch, TensorFlow, ONNX, and PaddlePaddle across Intel CPUs, GPUs, and NPUs without the training framework. Adds a GenAI pipeline for LLMs plus Hugging Face, vLLM, and LangChain integrations.
AI Model2018
Provides unified model definitions and a single API for pretrained text, vision, audio, and multimodal models for both training and inference. Emphasizes cross-framework compatibility (PyTorch/TF/JAX), pipeline-based inference, and direct access to 1M+ Hub checkpoints.
AI Model2018
Turns model definitions into a shared layer across training and inference stacks, covering text, vision, audio, video, and multimodal models. Pipelines, Trainer, and generation APIs make pretrained models usable without locking teams to one framework.
AI Coding2018
Differentiable programming framework for quantum computers: build variational circuits, compute their gradients alongside PyTorch, TensorFlow, or JAX, and run identical code on simulators or real hardware via IBM, AWS Braket, and Google plugins.
Chops any layer-sequence model across accelerators and splits each mini-batch into micro-batches to keep the pipeline busy, hitting near-linear speedup without architecture-specific tricks or fast interconnects.
AI Infra2018
Turns NumPy-style Python into differentiable, compiled, vectorized programs for CPU, GPU, and TPU. Its leverage is composable transformations: grad, jit, vmap, and sharding combine instead of living in separate APIs.