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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Technical
Normalizing 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…
Visual AIWasserstein GAN
Wasserstein GAN (WGAN) is a redesign of the GAN training objective that uses the Wasserstein distance instead of the original min-max loss.
Visual AIConditional GANs
Conditional GANs (cGANs) extend ordinary GANs by feeding extra information, like a class label or text, into both the generator and discriminator.
Visual AIPix2Pix Image-to-Image Translation
Pix2Pix is a conditional GAN that learns to translate one type of image into another, such as turning a sketch into a photo or a map into a satellite view.
Visual AIImage Colorization
Image colorization uses AI to add plausible, realistic color to black-and-white photos and film.
Visual AIStructure from Motion
Structure from Motion (SfM) reconstructs 3D scene geometry and camera positions from a set of overlapping 2D photos taken from different viewpoints.
Visual AIMulti-View Stereo
Multi-View Stereo (MVS) takes many calibrated photos of a scene and produces a dense 3D reconstruction by estimating depth at nearly every pixel.
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…
CompaniesIBM Granite Models
Granite is IBM's family of open, enterprise-focused AI models built for business tasks like coding, document understanding, and retrieval.
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