This repository contains a curated collection of data science/analysis and AI concepts and IQs, shared on my Threads page @AIinMinutes. Topics range from foundational mathematics to cutting-edge generative AI concepts, aiming to support learners and professionals preparing for various data science roles. 📚
The book based on this repo is currently under development. Check it out here: AI in Minutes.
| Concept # | Concept Name | Notebook |
|---|---|---|
| 1 | Causal Attention | View Notebook |
| 2 | Text Decoding Strategies: Greedy vs Beam | View Notebook |
| 3 | Layer vs RMS Normalization | View Notebook |
| 4 | Multi-head Attention | View Notebook |
| 5 | Energy | View Notebook |
| 6 | Gaussian Mixture Models | View Notebook |
| 7 | Hyperplanes | View Notebook |
| 8 | Inner Product | View Notebook |
| 9 | Moore Penrose Inverse | View Notebook |
| 10 | Jacobians and Gradients behind Multi-class Classification | View Notebook |
| 11 | Norm and Metric | View Notebook |
| 12 | Rank One Matrices | View Notebook |
| 13 | Auto-encoder Latent Space | View Notebook |
| 14 | PCA for Anomaly Detection | View Notebook |
| 15 | Variational AutoEncoder for Anomaly Detection | View Notebook |
| 16 | Variational AutoEncoder Loss Function | View Notebook |
| 17 | Attention Mechanism | View Notebook |
| 18 | GELU | View Notebook |
| 19 | Orthogonality | View Notebook |
| 20 | Perplexity | View Notebook |
| Concept # | Concept Name | Notebook |
|---|---|---|
| 1 | Gini Impurity vs Entropy | View Notebook |
| 2 | Agglomerative Clustering | View Notebook |
| 3 | Elastic Net | View Notebook |
| 4 | Huber Loss | View Notebook |
| 5 | Mahalanobis Distance | View Notebook |
| 6 | Natural Breaks | View Notebook |
| 7 | Oversampling | View Notebook |
| 8 | PCA vs Feature Agglomeration | View Notebook |
| 9 | Permutation Importance | View Notebook |
| 10 | Pseudo R^2 | View Notebook |
| Concept # | Concept Name | Notebook |
|---|---|---|
| 1 | Balanced Focal Loss | View Notebook |
| 2 | Jensen's Inequality | View Notebook |
| 3 | Reparametrization Trick | View Notebook |
| 4 | Temperature Scaled Softmax | View Notebook |
| Concept # | Concept Name | Notebook |
|---|---|---|
| 1 | Logistic Regression Coefficient Interpretation | View Notebook |
| 2 | Shapley values and SHAP for ML | View Notebook |
| 3 | Counterfactuals | View Notebook |
| Concept # | Concept Name | Notebook |
|---|---|---|
| 1 | Autocorrelation Function vs Partial Autocorrelation Function | View Notebook |
| 2 | Adjusted R^2 | View Notebook |
| 3 | Condition Number | View Notebook |
| 4 | Cramer's V | View Notebook |
| 5 | Exponentially Weighted Average and Bias Correction | View Notebook |
| 6 | Kendall's Tau Rank Correlation | View Notebook |
| 7 | Kruskal Wallis | View Notebook |
| 8 | Spurious Correlation | View Notebook |
| 9 | Leave One Out Cross Validation and PRESS | View Notebook |
| Concept # | Concept Name | Notebook |
|---|---|---|
| 1 | Canonical Correlation Analysis | View Notebook |
| 2 | Correspondence Analysis | View Notebook |
| 3 | Factor Analysis | View Notebook |
| 4 | Hotelling's T^2 | View Notebook |
| 5 | Principal Component Analysis | View Notebook |
| Concept # | Concept Name | Notebook |
|---|---|---|
| 1 | Chebyshev's Inequality | View Notebook |
| 2 | Distribution of Minimum | View Notebook |
| 3 | Matrix Calculus Jacobians and Gradients | View Notebook |
| 4 | Multivariate Normal Distribution | View Notebook |
| 5 | Mutual Information | View Notebook |
| 6 | Point Biserial Correlation Coefficient | View Notebook |
| 7 | Unbiasesd vs Consistent Estimator | View Notebook |
| 8 | ECDF | View Notebook |
| Concept # | Concept Name | Notebook |
|---|---|---|
| 1 | User Item Interaction Matrix | View Notebook |
| Concept # | Concept Name | Notebook |
|---|---|---|
| 1 | Spectral Decomposition | View Notebook |
| Concept # | Concept Name | Notebook |
|---|---|---|
| 1 | Kadane's Algorithm | View Notebook |
| 2 | Prefix Sum and Sliding Window | View Notebook |
| 3 | Pivoting in Pandas | View Notebook |
| Concept # | Concept Name | Notebook |
|---|---|---|
| 1 | Plotnine: Python's ggplot2 | View Notebook |
I follow a structured approach to sharing knowledge on Threads, posting concepts and thought-provoking questions (IQs) that stem from my professional experience, academic background, and interview scenarios. These questions are either ones I have encountered, been asked in interviews, or would consider posing in a data science discussion. I refine each question to maximize conceptual coverage and, at times, deliberately choose intellectually stimulating topics to encourage deeper engagement.
Contributions are welcome! If you have suggestions for new questions, additional resources, or improvements to the current answers, feel free to submit a pull request or open an issue.
- Code in this repository is licensed under the MIT License.
- Content (text, explanations, visualizations, etc.) is licensed under Creative Commons Attribution 4.0 (CC BY 4.0).
This means:
- You are free to use and modify the code as per the MIT license.
- You may reuse and share content, but you must provide proper attribution.
For details, check the LICENSE file.
Email: AIinMinutes@icloud.com
For more updates, follow me on Threads @AIinMinutes.