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Your Entire Model Improvement Efforts Might Be Going in Vain

Your Entire Model Improvement Efforts Might Be Going in Vain

...and here's how to avoid it.

Why Sklearn’s Logistic Regression Has no Learning Rate Hyperparameter?

Why Sklearn’s Logistic Regression Has no Learning Rate Hyperparameter?

What are we missing here?

MLE vs. EM — What’s the Difference?

MLE vs. EM — What’s the Difference?

A popular interview question.

Decision Trees ALWAYS Overfit! Here's a Neat Technique to Prevent It

Decision Trees ALWAYS Overfit! Here's a Neat Technique to Prevent It

Balancing cost and model size.

A Critical Feature Engineering Direction That Many ML Models Forget to Explore

A Critical Feature Engineering Direction That Many ML Models Forget to Explore

Understanding cyclical feature engineering.

PyTorch Models Are Not Entirely Deployment-Friendly

PyTorch Models Are Not Entirely Deployment-Friendly

Eliminating the dependence of PyTorch models on Python.

The First Step to Feature Scaling is NOT Feature Scaling

The First Step to Feature Scaling is NOT Feature Scaling

Approaching feature scaling the right way.

A Common Mistake That Many Spark Programmers Commit and Never Notice

A Common Mistake That Many Spark Programmers Commit and Never Notice

Extend your learnings from Pandas to Spark with caution.

One-Hot Encoding Introduces a Serious Problem in The Dataset

One-Hot Encoding Introduces a Serious Problem in The Dataset

Hint: This is NOT about sparse data representation.

Gradient Checkpointing: Reduce Memory Usage by At least 50-60% When Training a Neural Network

Gradient Checkpointing: Reduce Memory Usage by At least 50-60% When Training a Neural Network

An underrated technique to train larger ML models.

Most People Don’t Entirely Understand How Dropout Works

Most People Don’t Entirely Understand How Dropout Works

Here's the remaining information which you must know.

The Biggest Source of Friction in Developing ML Models That Most Data Scientists Overlook

The Biggest Source of Friction in Developing ML Models That Most Data Scientists Overlook

The guide that every everyone must read to manage ML experiments like a pro.

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