You can build a RAG system with just 4 lines of code using @clarifai Python SDK. Below is the entire code. Let's look at it step by step:
@sumanth_077
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Build RAG LLM App Chat PDF Files Gemini Flash
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Build a LLM app with RAG to chat with PDF files using Gemini Flash in just 4 lines of Python code (step-by-step instructions):
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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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Eigenvectors and Principal Components for 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 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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Machine Learning for Beginners: Complete Guide to ML Techniques and Projects
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3. Machine Learning for Beginners This covers and helps you learn: – Various ML Techniques
– Building your first ML Project right from data collection to create a web app for a trained model
– NLP Techniques
– Times Series
– Reinforcement Learning https://
microsoft.github.io/ML-For-Beginne
rs/#/
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Data Science for Beginners: Complete Learning Guide and Resources
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2. Data Science for Beginners This covers and helps you learn: – Data Science Basics & Ethics
– Statistics
– SQL & NOSQL
– Data Preparation & Visualization
– Data Analysis
– Complete Data Science Lifecycle Check this out: https://
microsoft.github.io/Data-Science-F
or-Beginners/#/
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