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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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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.
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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
Attention Rollout and Head Pruning
Attention rollout is a method for tracing how information flows through a Transformer's stacked attention layers to explain which input tokens influence…
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.
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…
TechnicalLinear Probing and Frozen Feature Evaluation
Linear probing tests how good a pretrained model's internal representations are by freezing the network and training only a simple linear classifier on top.
TechnicalDenseNet and Dense Connectivity
DenseNet is a convolutional network where every layer receives the feature maps of all preceding layers as input.
TechnicalBottleneck Architectures
A bottleneck architecture squeezes data through a narrow intermediate layer before expanding it again, forcing the network to learn compact, efficient…
TechnicalGradient Accumulation
Gradient accumulation lets you simulate a large batch size on limited GPU memory by summing gradients over several small mini-batches before updating…
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