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distill.pub | ||
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blog.quipu-strands.com
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| | | | | [AI summary] The text presents an extensive overview of Bayesian optimization techniques, focusing on their applications in black-box function optimization, including challenges and solutions such as computational efficiency, scalability, and integration with deep learning models. It also highlights key research contributions and references to seminal papers and authors in the field. | |
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francisbach.com
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blog.fastforwardlabs.com
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| | | | | By Chris and Melanie. The machine learning life cycle is more than data + model = API. We know there is a wealth of subtlety and finesse involved in data cleaning and feature engineering. In the same vein, there is more to model-building than feeding data in and reading off a prediction. ML model building requires thoughtfulness both in terms of which metric to optimize for a given problem, and how best to optimize your model for that metric! | |
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scorpil.com
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| | | In Part One of the "Understanding Generative AI" series, we delved into Tokenization - the process of dividing text into tokens, which serve as the fundamental units of information for neural networks. These tokens are crucial in shaping how AI interprets and processes language. Building upon this foundational knowledge, we are now ready to explore Neural Networks - the cornerstone technology underpinning all Artificial Intelligence research. A Short Look into the History Neural Networks, as a technology, have their roots in the 1940s and 1950s. | ||
