Not sure about R, but there are some really good tutorials on CleanLab
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Daily Content Sharing on Python, Data Science, and Machine Learning
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That's a wrap! If you are interested in any of these below topics: – Python – Data Science – Machine Learning – Data Analysis – LLMs – MLOps Find me → @Sumanth_077 I'm sharing daily content over here.
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Implementing PCA Transform and Inverse Transform Methods
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Now define the "transform" method that transforms the input data into a lower-dimensional representation using the calculated eigenvectors. Finally the "inverse_transform" method to reconstructs the reduced data back to the original space using eigenvectors and mean.
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Implementing PCA: Fit Method with Eigenvalue Sorting
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Now define the "fit" method that takes in that data and calculates the mean, normalizes the data, computes the covariance matrix, eigenvalues, and eigenvectors. Then the Eigenvectors are sorted based on eigenvalues. You may ask how, eigen vectors does dimensionality reduction?
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Implementing PCA Class with NumPy and Initialization Setup
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First Import numpy and define a class named "PCA" to encapsulate the operations. In the __init__ constructor let's initialize the number of components to reduce to, which is n_components, and similarly create placeholders for mean and eigenvectors.
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Principal Component Analysis PCA Explained and Implemented in Python
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Princiapal Component Analysis (PCA) clearly explained and implemented from scratch in Python:
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Smart Contracts Best Practices on Ethereum Blockchain
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Smart contracts are digital programs on blockchains like Ethereum that execute tasks automatically when conditions are met. Here are some best practices to follow. Microblog @antgrasso #SmartContracts #blockchain
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Python ML NLP LLMs AI Engineering Tutorials Daily
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If you interested in: – Python – ML/MLOps – CV/NLP – LLMs – AI Engineering Find me → @akshay_pachaar done
Everyday, I share tutorials on above topics! Cheers! -
AI Tool Automatically Detects Spelling Errors Inaccuracies Prompts
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It enables you to automatically detect: – Spelling errors
– Factual inaccuracies
– Incomplete/vague prompts
– Context-based inaccuracies
– And more … Read the full article here: -
Automated Techniques Identify Low-Quality Data in Instruction Tuning
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I recently read a great article that showcases automated techniques to identify and weed out low-quality data in any instruction tuning dataset. They even put the famous Dolly-15k dataset under the microscope, and the findings are eye-opening! continue reading ….