π The Machine Learning Lessons Iβve Learned Last Month
π Category: MACHINE LEARNING
π Date: 2026-02-09 | β±οΈ Read time: 5 min read
Delayed January: deadlines, downtimes, and flow times
#DataScience #AI #Python
π Category: MACHINE LEARNING
π Date: 2026-02-09 | β±οΈ Read time: 5 min read
Delayed January: deadlines, downtimes, and flow times
#DataScience #AI #Python
π Loss Functions in Machine Learning
Choosing the right loss function is not a minor detail. It directly shapes how a model learns, converges, and performs in production.
Regression and classification problems require very different optimization signals.
π Regression intuition
- MSE and RMSE strongly penalize large errors, which helps when large deviations are costly, such as demand forecasting.
- MAE and Huber Loss handle noise better, which works well for sensor data or real world measurements with outliers.
- Log-Cosh offers smooth gradients and stable training when optimization becomes sensitive.
π Classification intuition
- Binary Cross-Entropy is the default for yes or no problems like fraud detection.
- Categorical Cross-Entropy fits multi-class problems such as image or document classification.
- Sparse variants reduce memory usage when labels are integers.
- Hinge Loss focuses on decision margins and is common in SVMs.
- Focal Loss shines in imbalanced datasets like rare disease detection by focusing on hard examples.
Example:
For a credit card fraud model with extreme class imbalance, Binary Cross-Entropy often underperforms. Focal Loss shifts learning toward rare fraud cases and improves recall without sacrificing stability.
Loss functions are not interchangeable. They encode assumptions about data, noise, and business cost.
Choosing the correct one is a modeling decision, not a framework default.
https://t.iss.one/DataScienceM
Choosing the right loss function is not a minor detail. It directly shapes how a model learns, converges, and performs in production.
Regression and classification problems require very different optimization signals.
π Regression intuition
- MSE and RMSE strongly penalize large errors, which helps when large deviations are costly, such as demand forecasting.
- MAE and Huber Loss handle noise better, which works well for sensor data or real world measurements with outliers.
- Log-Cosh offers smooth gradients and stable training when optimization becomes sensitive.
π Classification intuition
- Binary Cross-Entropy is the default for yes or no problems like fraud detection.
- Categorical Cross-Entropy fits multi-class problems such as image or document classification.
- Sparse variants reduce memory usage when labels are integers.
- Hinge Loss focuses on decision margins and is common in SVMs.
- Focal Loss shines in imbalanced datasets like rare disease detection by focusing on hard examples.
Example:
For a credit card fraud model with extreme class imbalance, Binary Cross-Entropy often underperforms. Focal Loss shifts learning toward rare fraud cases and improves recall without sacrificing stability.
Loss functions are not interchangeable. They encode assumptions about data, noise, and business cost.
Choosing the correct one is a modeling decision, not a framework default.
https://t.iss.one/DataScienceM
β€3
Effective Pandas 2: Opinionated Patterns for Data Manipulation
This book is now available at a discounted price through our Patreon grant:
Original Price: $53
Discounted Price: $12
Limited to 15 copies
Buy: https://www.patreon.com/posts/effective-pandas-150394542
This book is now available at a discounted price through our Patreon grant:
Discounted Price: $12
Limited to 15 copies
Buy: https://www.patreon.com/posts/effective-pandas-150394542
β€1
π Implementing the Snake Game in Python
π Category: PROGRAMMING
π Date: 2026-02-10 | β±οΈ Read time: 17 min read
An easy step-by-step guide to building the snake game from scratch
#DataScience #AI #Python
π Category: PROGRAMMING
π Date: 2026-02-10 | β±οΈ Read time: 17 min read
An easy step-by-step guide to building the snake game from scratch
#DataScience #AI #Python
π How to Personalize Claude Code
π Category: LLM APPLICATIONS
π Date: 2026-02-10 | β±οΈ Read time: 8 min read
Learn how to get more out of Claude code by giving it access to moreβ¦
#DataScience #AI #Python
π Category: LLM APPLICATIONS
π Date: 2026-02-10 | β±οΈ Read time: 8 min read
Learn how to get more out of Claude code by giving it access to moreβ¦
#DataScience #AI #Python
Forwarded from Machine Learning with Python
π¨π»βπ» When I was just starting out and trying to get into the "data" field, I had no one to guide me, nor did I know what exactly I should study. To be honest, I was confused for months and felt lost.
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π How to Model The Expected Value of Marketing Campaigns
π Category: DATA SCIENCE
π Date: 2026-02-10 | β±οΈ Read time: 9 min read
The approach that takes companies to the next level of data maturity
#DataScience #AI #Python
π Category: DATA SCIENCE
π Date: 2026-02-10 | β±οΈ Read time: 9 min read
The approach that takes companies to the next level of data maturity
#DataScience #AI #Python
β€5
π Not All RecSys Problems Are Created Equal
π Category: MACHINE LEARNING
π Date: 2026-02-11 | β±οΈ Read time: 9 min read
How baseline strength, churn, and subjectivity determine complexity
#DataScience #AI #Python
π Category: MACHINE LEARNING
π Date: 2026-02-11 | β±οΈ Read time: 9 min read
How baseline strength, churn, and subjectivity determine complexity
#DataScience #AI #Python
β€3
π Building an AI Agent to Detect and Handle Anomalies in Time-Series Data
π Category: AGENTIC AI
π Date: 2026-02-11 | β±οΈ Read time: 13 min read
Combining statistical detection with agentic decision-making
#DataScience #AI #Python
π Category: AGENTIC AI
π Date: 2026-02-11 | β±οΈ Read time: 13 min read
Combining statistical detection with agentic decision-making
#DataScience #AI #Python
β€1
π AI in Multiple GPUs: Understanding the Host and Device Paradigm
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2026-02-12 | β±οΈ Read time: 7 min read
Learn how CPU and GPUs interact in the host-device paradigm
#DataScience #AI #Python
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2026-02-12 | β±οΈ Read time: 7 min read
Learn how CPU and GPUs interact in the host-device paradigm
#DataScience #AI #Python
π How to Leverage Explainable AI for Better Business Decisions
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2026-02-12 | β±οΈ Read time: 10 min read
Moving beyond the black box to turn complex model outputs into actionable organizational strategies.
#DataScience #AI #Python
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2026-02-12 | β±οΈ Read time: 10 min read
Moving beyond the black box to turn complex model outputs into actionable organizational strategies.
#DataScience #AI #Python
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π The Evolving Role of the ML Engineer
π Category: AUTHOR SPOTLIGHTS
π Date: 2026-02-13 | β±οΈ Read time: 5 min read
Stephanie Kirmer on the $200 billion investment bubble, how AI companies can rebuild trust, andβ¦
#DataScience #AI #Python
π Category: AUTHOR SPOTLIGHTS
π Date: 2026-02-13 | β±οΈ Read time: 5 min read
Stephanie Kirmer on the $200 billion investment bubble, how AI companies can rebuild trust, andβ¦
#DataScience #AI #Python
π AI in Multiple GPUs: Point-to-Point and Collective Operations
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2026-02-13 | β±οΈ Read time: 10 min read
Learn PyTorch distributed operations for multi GPU AI workloads
#DataScience #AI #Python
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2026-02-13 | β±οΈ Read time: 10 min read
Learn PyTorch distributed operations for multi GPU AI workloads
#DataScience #AI #Python