📌 Mechanistic Interpretability: Peeking Inside an LLM

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-02-05 | ⏱️ Read time: 19 min read

Are the human-like cognitive abilities of LLMs real or fake? How does information travel through…

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📌 Why Is My Code So Slow? A Guide to Py-Spy Python Profiling

🗂 Category: PROGRAMMING

🕒 Date: 2026-02-05 | ⏱️ Read time: 10 min read

Stop guessing and start diagnosing performance issues using Py-Spy

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📌 The Rule Everyone Misses: How to Stop Confusing loc and iloc in Pandas

🗂 Category: DATA SCIENCE

🕒 Date: 2026-02-05 | ⏱️ Read time: 9 min read

A simple mental model to remember when each one works (with examples that finally click).

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📌 Pydantic Performance: 4 Tips on How to Validate Large Amounts of Data Efficiently

🗂 Category: DATA ENGINEERING

🕒 Date: 2026-02-06 | ⏱️ Read time: 8 min read

The real value lies in writing clearer code and using your tools right

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📌 Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes

🗂 Category: AGENTIC AI

🕒 Date: 2026-02-06 | ⏱️ Read time: 32 min read

How much of your AI agent’s output is real data versus confident guesswork?

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📌 What I Am Doing to Stay Relevant as a Senior Analytics Consultant in 2026

🗂 Category: DATA ANALYSIS

🕒 Date: 2026-02-07 | ⏱️ Read time: 7 min read

Learn how to work with AI, while strengthening your unique human skills that technology cannot…

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🚀 Machine Learning Workflow: Step-by-Step Breakdown
Understanding the ML pipeline is essential to build scalable, production-grade models.

👉 Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.

👉 Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.

👉 Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.

👉 Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.

👉 Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.

👉 Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.

👉 Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.

👉 Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.

👉 Model Evaluation
Use task-specific metrics:
- Classification – MCC, Sensitivity, Specificity, Accuracy
- Regression – RMSE, R², MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.

💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.

https://t.iss.one/DataScienceM

📌 The Death of the “Everything Prompt”: Google’s Move Toward Structured AI

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-02-09 | ⏱️ Read time: 16 min read

How the new Interactions API enables deep-reasoning, stateful, agentic workflows.

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📌 The Machine Learning Lessons I’ve Learned Last Month

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-02-09 | ⏱️ Read time: 5 min read

Delayed January: deadlines, downtimes, and flow times

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🚀 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.

👉 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.

👉 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

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

📌 Implementing the Snake Game in Python

🗂 Category: PROGRAMMING

🕒 Date: 2026-02-10 | ⏱️ Read time: 17 min read

An easy step-by-step guide to building the snake game from scratch

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📌 How to Personalize Claude Code

🗂 Category: LLM APPLICATIONS

🕒 Date: 2026-02-10 | ⏱️ Read time: 8 min read

Learn how to get more out of Claude code by giving it access to more…

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