Snowflake Arctic Embed Yesterday @SnowflakeDB shipped their open source Arctic family of embedding models. You can use them with our @huggingface Embeddings connector to use these exceptionally performant models locally. Python docs: https://
python.langchain.com/docs/integrati
ons/providers/snowflake/#embedding-models
… Snowflake
MACHINE LEARNING
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Snowflake Arctic Embed Open Source Models Launch
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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 Transform and Inverse Transform Methods for PCA
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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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Eigenvectors and Principal Components for Data Dimensionality Reduction
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Eigen vectors helps in identifying the directions of highest variance in the data, which are then used to construct principal components. These principal components allows you to represent the data in a lower-dimensional while saving as much meaningful information as possible.
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Implementing PCA fit method with eigenvalue decomposition and 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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Creating a PCA Class Constructor with NumPy for Dimensionality Reduction
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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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PCA Implementation From Scratch in Python With Step-by-Step Code
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PCA implemented from scratch in Python! — step by step explanation with code–
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Mixtral excels in science, code, and multilingual tasks
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Mixtral particularly shines in science, code, and multilingual (below are performance results for the base model). 2/n
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Mixtral 8x22B Instruct Released with Superior Performance
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Mixtral 8x22B Instruct is out. It significantly outperforms existing open models, and only uses 39B active parameters (making it significantly faster than 70B models during inference). 1/n
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Essential Guide to Data Science and Digital Transformation
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Explore the essentials of #DataScience with this guide. For more tech insights and to boost your digital savvy, follow @ingliguori and check out 'The Digital Edge'. https://
bit.ly/3u4pILl #AI #BigData #DigitalTransformation