The effectiveness of experiments is based on the organizational and technical side of the work. Novice engineers who are engaged in neural networks and train models make fairly common mistakes. For example, getting carried away with enumerating and tuning machine learning models, they miss the most important stage of data preparation, do not think about how to make experiments reproducible, and the programming stage is fast. Let's talk about this - how to experiment effectively with neural networks.
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