Microsoft's successor to AutoGen and Semantic Kernel for agents and graph-based workflows.
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A concrete implementation of durable execution, retries, and human approval for an agent workflow.
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Risks and mitigations for developing and deploying generative AI applications.
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A vendor-neutral specification for agent discovery, task delegation, streaming, asynchronous updates, and cross-platform communication.
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Mathematical foundations by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
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Deep learning foundations and applications.
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An introductory path through generative AI concepts and Google Cloud tooling.
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Focused courses on current generative AI engineering techniques.
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robust introduction to the subject and also the foundation for a Data Analyst “nanodegree” certification sponsored by Facebook and MongoDB.
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Roadmaps featuring essential concepts, learning methods, and the tools to put them into practice.
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