30초 요약
Jeremy Howard recommends hands-on experiments, fine-tuning and combining deep learning with domain knowledge. Course examples and timing reflect the 2019 conversation.
- 1Run experimentsTrain models and inspect their behavior.
- 2Work with dataBuild, clean and inspect a relevant dataset.
- 3Choose a domainDomain knowledge helps evaluate usefulness and errors.
핵심 정리
- Train models and inspect inputs and outputs.
- Use data connected to a problem you care about.
- Domain knowledge helps interpret errors.
챕터별로 다듬은 전문 읽기
Learn by training
Jeremy Howard advises beginners in deep learning to train models and run many experiments rather than only read about them.
Jeremy Howard advises beginners in deep learning to train models and run many experiments rather than only read about them.
He recommends inspecting inputs and outputs, changing the inputs and observing how results vary to build intuition.
Howard argues for fine-tuning a model on data from a domain the learner cares about. The time and resources required vary with the task, data and hardware.
Build and inspect a dataset
He describes a fast.ai course example in which learners build an image dataset, clean examples and create a small classification application. Data collection and reuse require appropriate permissions.
He describes a fast.ai course example in which learners build an image dataset, clean examples and create a small classification application. Data collection and reuse require appropriate permissions.
He shares examples of student projects and emphasizes inspecting mistakes, not only reporting accuracy.
Develop domain expertise
Asked how to become an expert over several years, Howard again recommends training models in a domain that matters to the learner.
Asked how to become an expert over several years, Howard again recommends training models in a domain that matters to the learner.
He gives examples such as medical analysis, language and fisheries, arguing that domain expertise helps identify useful questions and evaluate results.
Fridman and Howard agree that working on a real problem helps researchers understand whether results are useful and why errors occur.
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