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.
MACHINE LEARNING
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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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Cleanlab: Essential ML Training Data Quality Tool
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Cleanlab is a must ML training process
Thanks for sharing -
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! -
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 ….
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Clean Data: The Critical Moat for LLM Performance
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Clean data is the biggest MOAT, in the age of AI! LLM finetuning datasets often contain bad examples, which are harder to detect manually & become a major performance blocker. But there's a solution:
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Random Fourier Features Bridge Gaussian Processes and Neural Networks
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Agreed, they're very widely used there. Although if you start using e.g. random fourier features approximations to fit them then you start getting NN like models … and all the techniques of NNs can be imported to help with scale.
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Neural Networks as Gaussian Processes in the Infinite Width Limit
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The sort of mathematics we can do with NNs is to take limits of layer sizes such that the start to behave like GPs. Then we understand how the NN is working in that limit.
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Gaussian Processes: Mathematical Clarity vs Neural Network Complexity
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The mathematical understanding of the model. It's extremely well characterised because everything's Gaussian. The mathematics of NNs is so complex that modern "ML experts" go on TV and tell everyone that "we don't understand how they work". We do understand how GPs work.
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Understanding ML Expertise: Dismissing Alternative Approaches Due to Deployment Pressure
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And its understandable that people who are in a rush to deploy these methods dismiss other ideas as cults or ineffective, because the breadth of what you need to know to be a true expert in ML is pretty intimidating.