CODE

→ View original post on X — @skirano

Stability API Documentation Download Feature for LLM Implementation

By

@skirano

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17 April 2024 17h53

I love how the new Stability API page has a button to download the entire specification. Easiest way to feed it to an LLM for quick implementation and testing. Every documentation page should have this.

→ View original post on X — @jxmnop

Pandas DataFrame Cloud Sharing Syntax Innovation

By

@jxmnop

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17 April 2024 17h50

i want this syntax: > df = pd.DataFrame(…)
> http://
df.to_cloud() https://
website-with-your-df.com/a0283u0j

→ View original post on X — @jxmnop

Cloud Data Sharing Syntax for Pandas DataFrames

By

@jxmnop

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17 April 2024 17h15

i want this syntax: > df = pd.DataFrame(…)
> http://
df.to_cloud() https://
website-with-your-df.com/a0283u0j

→ View original post on X — @mattshumer_

AI Code Generation Quality Declining Frequently

By

@mattshumer_

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17 April 2024 17h07

– It's starting to do the whole "add this code here" thing where it's asking me to do the work — hadn't seen that before… now it's happening frequently
– I'm giving it simple code snippets for it to weave together into a system. Used to work perfectly on the first shot. Now it

Daily Content Sharing on Python, Data Science, and Machine Learning

By

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17 April 2024 16h35

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.

Implementing Transform and Inverse Transform Methods for PCA

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17 April 2024 16h35

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.

Implementing PCA fit method with eigenvalue decomposition and sorting

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17 April 2024 16h35

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?

Creating a PCA Class Constructor with NumPy for Dimensionality Reduction

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17 April 2024 16h35

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.