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Machine Learning

A branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

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Boosting Authorization Rates: Partnering Strategies and Intelligent Retries

Boosting Authorization Rates: Partnering Strategies and Intelligent Retries

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8 min read
Generative vs Discriminative Models: The Artist Who Paints vs The Critic Who Points

Generative vs Discriminative Models: The Artist Who Paints vs The Critic Who Points

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9 min read
The Curse of Dimensionality: Why More Features Can Destroy Your Model Instead of Saving It

The Curse of Dimensionality: Why More Features Can Destroy Your Model Instead of Saving It

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9 min read
Regularization: The Art of Telling Your Model to Calm Down and Stop Overthinking

Regularization: The Art of Telling Your Model to Calm Down and Stop Overthinking

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10 min read
L1 vs L2 Regularization: The Minimalist vs The Diplomat — Two Philosophies That Shape Your Model

L1 vs L2 Regularization: The Minimalist vs The Diplomat — Two Philosophies That Shape Your Model

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10 min read
hidden layer - day 02 of dl

hidden layer - day 02 of dl

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2 min read
Cross-Validation: Why Testing Your Model Once Is Like Judging a Restaurant by a Single Bite

Cross-Validation: Why Testing Your Model Once Is Like Judging a Restaurant by a Single Bite

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11 min read
Learning Rate: The One Number That Can Make or Break Your Entire Model

Learning Rate: The One Number That Can Make or Break Your Entire Model

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10 min read
Batch vs Mini-Batch vs Stochastic Gradient Descent: Three Hikers, Three Strategies, One Mountain

Batch vs Mini-Batch vs Stochastic Gradient Descent: Three Hikers, Three Strategies, One Mountain

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10 min read
Gradient Descent: How to Find the Lowest Point in a Valley While Completely Blindfolded

Gradient Descent: How to Find the Lowest Point in a Valley While Completely Blindfolded

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10 min read
Classification vs Regression: The Doctor Who Gives Answers vs The Doctor Who Gives Numbers

Classification vs Regression: The Doctor Who Gives Answers vs The Doctor Who Gives Numbers

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9 min read
Loss Functions: The Brutally Honest Friend Your Model Desperately Needs

Loss Functions: The Brutally Honest Friend Your Model Desperately Needs

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9 min read
Parametric vs Non-Parametric Models: The GPS vs The Taxi Driver

Parametric vs Non-Parametric Models: The GPS vs The Taxi Driver

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9 min read
Overfitting & Underfitting: Why Your Model Aced the Practice Test But Failed the Real Exam

Overfitting & Underfitting: Why Your Model Aced the Practice Test But Failed the Real Exam

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10 min read
The Bias-Variance Tradeoff: Why Your Model is Either Too Dumb or Too Smart

The Bias-Variance Tradeoff: Why Your Model is Either Too Dumb or Too Smart

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7 min read
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