01
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.
02
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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Language AI
Typical Sampling
Typical sampling is a text-generation method that picks the next word from tokens whose information content sits close to the model's expected surprise…
Language AIMirostat Perplexity Control
Mirostat is a decoding algorithm that actively steers a language model's output toward a target perplexity (a set level of surprise) using a feedback loop.
Language AISycophancy in Language Models
Sycophancy is the tendency of AI language models to tell users what they want to hear, agreeing with stated opinions or caving to pushback even…
TechnicalSecond-Order Optimization and Newton Methods
Second-order optimization uses curvature information (the Hessian matrix of second derivatives) to take smarter steps toward a minimum, not just the slope.
FundamentalsWeight Decay and L2 Regularization
Weight decay is a simple, powerful technique that nudges a model's weights toward zero during training, discouraging it from relying too heavily on any…
FundamentalsDropout and Stochastic Regularization
Dropout is a regularization trick that randomly switches off a fraction of neurons during each training step, forcing the network to build redundant, robust…
TechnicalRMSNorm and Pre-Layer Normalization
RMSNorm is a lightweight normalization layer that rescales activations by their root mean square, and pre-layer normalization places that step before each…
TechnicalSwiGLU and Gated Activations
SwiGLU is a gated activation function that multiplies one linear projection of the input by a Swish-activated second projection, acting as a learnable…
TechnicalSqueeze-and-Excitation Networks
Squeeze-and-Excitation (SE) blocks let a convolutional network learn how much to weight each feature channel, recalibrating them based on global context.
TechnicalWarmup and Cosine Annealing Schedules
Warmup gently ramps the learning rate up from near zero before training, then cosine annealing smoothly decays it back down following a cosine curve.
TechnicalCyclical Learning Rates
Cyclical learning rates repeatedly cycle the learning rate up and down between a lower and upper bound instead of only decaying it.
TechnicalSharpness-Aware Minimization
Sharpness-Aware Minimization (SAM) is an optimization method that seeks not just a low loss but a low loss across a whole neighborhood of weights — a flat…
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