Preparing your Path…
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Path Catalog
Data is pulled live from GET /api/v1/paths, and only Paths with a published version are listed.
7801 Paths · page 266 / 781
This advanced learning path guides senior and graduate students through the core engineering principles behind augmented and virtual reality systems. It covers display technologies, optical systems, tracking, interaction, and real-time rendering, emphasizing the integration of these components into cohesive AR/VR experiences.
This graduate-level learning path explores the principles, architectures, and applications of edge computing. Starting with foundational distributed systems and cloud computing, it progresses through IoT integration, edge architectures, and advanced topics like security and machine learning at the edge, culminating in real-world applications and future directions.
This learning path guides senior or graduate students from foundational cryptographic and distributed systems concepts through the architecture of blockchain, consensus mechanisms, and smart contracts, culminating in an understanding of advanced topics and real-world applications. It is designed for learners with a computer engineering background who are interested in a deep, principled understanding of blockchain technology.
This graduate-level path provides a comprehensive understanding of neuromorphic computing, from biological neurons and spiking neural networks to hardware implementations and applications. Learners will explore the principles, design trade-offs, and real-world uses of neuromorphic systems, preparing them for advanced research or development.
A graduate-level learning path covering the foundational physics and computer engineering concepts needed to understand quantum computing, from qubits and quantum gates to algorithms and applications.
This learning path equips infrastructure engineers with the knowledge to architect and operate reliable data centers. It covers physical infrastructure, power and cooling systems, network design, and operational management. The path progresses from foundational concepts through advanced design and management practices.
This path equips performance engineers with a systematic methodology to evaluate and improve computer system performance. It covers foundational computer architecture, performance metrics, benchmarking, profiling, analytical modeling, and optimization techniques, culminating in practical application and assessment.
A professional learning path for system architects to master hardware-software co-design: partitioning, interface design, and optimization. Covers essential foundations in digital design, computer architecture, and software engineering, then dives into co-design methodologies, modeling, and verification.
A structured learning path for engineers and managers to plan, execute, and deliver computer engineering projects. Covers core project management, software engineering lifecycle, risk, communication, and delivery practices.
This advanced learning path equips cloud engineers with the knowledge and skills to implement DevOps practices in cloud environments. It covers core DevOps principles, cloud computing foundations, CI/CD pipelines, containerization, orchestration, and monitoring, emphasizing practical application and integration.