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AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
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Five outcome-based courses
Each course includes explicit outcomes, mapped competencies, practice activities, and an applied capstone.
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Responsible AI User
Use AI productively while protecting privacy, checking outputs, and preserving human accountability.
03
AI at Work
Evaluate workplace use cases, run safe pilots, measure value, and communicate changes responsibly.
04
AI Policy & Society
Analyze AI systems through rights, equity, governance, safety, and public-interest outcomes.
05
Building with AI Systems
Understand language models, retrieval, agents, evaluation, cost, and deployment safeguards through practical system design.
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FundamentalsCore concepts: how AI works at a high level.84 guides Language AILLMs, NLP, ChatGPT, prompting, and text understanding.155 guides Visual AIImage generation, computer vision, video, and design.133 guides Audio AIVoice, music, podcasting, and audio-first AI.117 guides CompaniesProfiles of OpenAI, Anthropic, Google, Meta, and more.121 guides ApplicationsHow AI shows up in coding, search, agents, and daily life.111 guides IndustriesAI in healthcare, finance, education, energy, and more.91 guides TechnicalChips, fine-tuning, RAG, vectors, and AI infrastructure.177 guides SocietyBias, privacy, copyright, safety, and the AI future.30 guides
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Technical
Sequence Parallelism and Ring Attention
Sequence parallelism splits a single long input sequence across multiple GPUs along the token (time) dimension, and Ring Attention lets those GPUs compute…
TechnicalActivation Recomputation Tradeoffs
Activation recomputation (gradient or activation checkpointing) saves GPU memory during training by discarding intermediate activations in the forward pass…
TechnicalGroup Relative Policy Optimization
Group Relative Policy Optimization (GRPO) is a reinforcement-learning method for fine-tuning language models that judges each answer against a group…
TechnicalNormalizing Flows
Normalizing flows are generative models that turn simple noise (like a Gaussian) into complex data through a chain of invertible, differentiable…
TechnicalEnergy-Based Models
Energy-based models (EBMs) learn a scalar 'energy' function that assigns low values to plausible data and high values to implausible data, defining…
TechnicalOptimizer State Offloading to CPU and NVMe
A memory-saving trick that parks the heavy bookkeeping of training (optimizer states, gradients, sometimes weights) in CPU RAM or on NVMe SSDs instead…
TechnicalCheckpoint Sharding and Resumable Training
Techniques for saving a model's training state in pieces (shards) so giant models can be saved and reloaded without choking on memory or disk limits, and so…
TechnicalGPU Memory Management and Fragmentation
How AI frameworks allocate, reuse, and reclaim the limited memory on a GPU, and why leftover gaps (fragmentation) can cause out-of-memory errors even…
TechnicalMulti-Instance GPU Partitioning
Multi-Instance GPU (MIG) is an NVIDIA technology that slices a single physical GPU into multiple isolated hardware partitions.
TechnicalGPU Scheduling and Cluster Orchestration
GPU scheduling decides which jobs run on which accelerators and when, while orchestration coordinates these jobs across an entire cluster of machines.
TechnicalSlurm for AI Training Clusters
Slurm is an open-source workload manager that schedules and runs jobs on high-performance computing clusters, and it has become a default choice for large AI…
TechnicalPlan-and-Solve Prompting
Plan-and-Solve (PS) prompting tells a language model to first devise an explicit plan and then carry it out step by step, fixing the failures that plain…
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