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lukesingham.com | ||
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teddykoker.com
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| | | | | A few posts back I wrote about a common parameter optimization method known as Gradient Ascent. In this post we will see how a similar method can be used to create a model that can classify data. This time, instead of using gradient ascent to maximize a reward function, we will use gradient descent to minimize a cost function. Lets start by importing all the libraries we need: | |
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isaacslavitt.com
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| | | | | [AI summary] The provided text is a detailed tutorial on using scikit-learn for machine learning tasks, including data preprocessing, model selection, cross-validation, and pipeline creation. It also touches on integrating R and Julia with Python through Jupyter notebooks. | |
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rasbt.github.io
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| | | | | A library consisting of useful tools and extensions for the day-to-day data science tasks. | |
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thomaslarock.com
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| | | [AI summary] Thomas LaRock, a data engineering leader and technical evangelist, discusses his career in building developer communities, driving data platform adoption, and current focus on machine learning and data analytics through his MS program. | ||
