Artificial Intelligence — A Modern Approach
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Artificial Intelligence Modern Approach Fourth Edition Book Recommendation
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Pretraining Vision and LLMs in Python on AWS
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Pretrain Vision and #LLMs (Large Language Models) in #Python — Techniques for building & deploying foundation models on #AWS: http://
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Advanced Deep Learning with Python Guide
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Advanced #DeepLearning with #Python: http://
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Python Data Science Solutions for Business Challenges and Analytics
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Dive into #DataScience — Use #Python To Tackle Tough Business Challenges: http://
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New LangChain Book: Building LLM Applications with Python and ChatGPT
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[New Book!] #GenerativeAI with #LangChain — Build Large Language Model (LLM) apps with #Python, ChatGPT, and other #LLMs: https://
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Pragmatic Machine Learning with Python: Deploy Models in Production
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"Pragmatic #MachineLearning with #Python: Learn How to Deploy Machine Learning Models in Production" Get it here: http://
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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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Fit Method: Computing Eigenvalues, Eigenvectors, and Dimensionality Reduction
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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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PCA Class Implementation: Initializing Components and Eigenvectors
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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 from Scratch
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Princiapal Component Analysis (PCA) clearly explained and implemented from scratch in Python: