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Learn to test and deploy machine learning projects with Sergey Karayev from The Full Stack. Topics include AB testing, software engineering, Docker, and serverless deployment.
Dive into AI and deep learning with Professor Pieter Abbeel in this less than 1-hour session from The Full Stack. Explore ImageNet, speech recognition, and more.
Dive into the foundations of large language models with The Full Stack's brief video, covering core ML, Transformer architecture, and notable LLMs.
Master the art of prompting language models with The Full Stack's bootcamp. Learn techniques like decomposition, reasoning, and reflection in under an hour.
Explore the future of AI and language models with The Full Stack's 1-2 hour video, discussing multimodality, scale limits, AGI presence, and safety measures.
Explore LangChain, a new LLM application framework, in this under 1-hour material from The Full Stack. Includes a demo and Q&A with the creator, Harrison Chase.
Explore the ethical dimensions of building ML-powered products with The Full Stack's 1-2 hour lecture, covering past, present, and future tech ethics.
Learn to build and manage ML teams, understand hiring in ML, and design ML products with The Full Stack's 1-2 hour material.
Explore Transformers, Large Language Models like GPT-3, prompt engineering, and more in this 1-2 hour material from The Full Stack.
Learn to build a continual learning system around a machine learning model with The Full Stack's 1-2 hour material, covering retraining strategies, metric monitoring, and testing.
Learn to transform ML models into products with The Full Stack's short video. Covers prototype deployment, various architectures, optimization, and edge deployment.
Learn data management from The Full Stack in under an hour. Topics include data storage, processing, annotation, and versioning, with a focus on best practices.
Learn to test ML codebases and troubleshoot deep neural networks in under an hour with The Full Stack. Gain knowledge on testing tools, clean code tools, and automation.
Learn to develop deep learning models with The Full Stack's concise guide, covering software engineering, distributed training, and experiment management in under an hour.
Learn when to use machine learning (ML) and how to select problems to solve with ML in this less than 1-hour material from The Full Stack.
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