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LLM + LSTM = Large Memory Models (LMMs)
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Final video reel - Made with Clipchamp.mp4
Feedback received for the Generative AI training from the students from University of texas

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AutomatedCleaning is a Python library for automated data cleaning. It helps preprocess and analyze datasets by handling missing values, outliers, spelling corrections, and more


Features

Supports both large (100+ GB) and small datasets

Detects and handles missing values and duplicate records

Identifies and corrects spelling errors in categorical values

Detect outliers

Detects and fixes data imbalance

Identifies and corrects skewness in numerical data

Checks for correlation and detects multicollinearity

Analyzes cardinality in categorical columns

Identifies and cleans text columns
Detect JSON-type columns

Performs univariate, bivariate, and multivariate analysis

https://lnkd.in/gmaStAsp
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Let's learn about Model Interpretability

Interpretability is essential for:

Model debugging - Why did my model make this mistake?

Feature Engineering - How can I improve my model

Detecting fairness issues - Does my model discriminate?

Human-AI cooperation - How can I understand and trust the model's decisions?

Regulatory compliance - Does my model satisfy legal requirements?

High-risk applications - Healthcare, finance, judicial,
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2025/06/30 07:21:28
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