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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
Depthwise Separable Convolutions
Depthwise separable convolutions factor a standard convolution into two cheaper steps, slashing the number of multiplications and parameters.
TechnicalDilated and Atrous Convolutions
Dilated convolutions (also called atrous convolutions) insert gaps between filter weights so a kernel covers a much larger area without adding parameters.
TechnicalHighway Networks and Skip Connections
Skip connections let information jump past layers, and highway networks were an early gated version of this idea.
TechnicalDomain Adaptation
Domain adaptation is a set of techniques for making a model trained on one kind of data (the source domain) work well on a different but related kind of data…
TechnicalInfiniBand and RDMA Networking
InfiniBand is a high-speed, low-latency interconnect that links servers and GPUs in AI clusters, and RDMA lets one machine read or write another's memory…
TechnicalCollective Communication and NCCL
Collective communication is how a group of GPUs exchange and combine data, and NCCL is NVIDIA's library that makes those exchanges blazingly fast.
TechnicalFeature Engineering Pipelines and Data Versioning
Feature engineering pipelines transform raw data into the numerical signals models actually learn from, while data versioning tracks exactly which data…
TechnicalOnline and Offline Feature Serving Skew
Training/serving skew happens when the features a model learns from offline differ from the features it actually receives in production, quietly wrecking…
TechnicalKubeflow and ML Pipeline Orchestration
Kubeflow is an open-source toolkit that runs machine learning workflows on Kubernetes, turning model training and deployment into reproducible, containerized…
TechnicalApache Airflow for ML Workflows
Apache Airflow is an open-source platform for authoring, scheduling, and monitoring workflows as code.
TechnicalMLflow and Model Lifecycle Tracking
MLflow is an open-source platform for managing the machine learning lifecycle, from experiment tracking to model packaging and deployment.
TechnicalExpert Parallelism for MoE Serving
Expert parallelism splits a Mixture-of-Experts model's many feed-forward 'experts' across different GPUs so each device holds only a slice of the parameters.
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