Data science/ML/AI

🧠 LLM Wiki LLM Wiki is a concept by Andrej Karpathy for maintaining a continuously evolving Markdown knowledge base instead of relying only on document retrieval. As information is processed, related topics are organized into linked wiki pages, making knowledge easier to navigate, update, and reuse over time. This is a thoughtful read if you're interested in how long-term knowledge systems for LLMs could evolve. 🔗 https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f

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Why Learning Rate Can Make or Break Training Think of gradient descent as walking downhill. The learning rate controls your step size. Too small? You'll eventually reach the bottom. It just takes forever. Too large? You'll keep overshooting the minimum. Sometimes you'll bounce back and forth without ever converging. That's why training loss can suddenly explode. Not because your model is bad. Because your optimizer is taking steps that are simply too big. The goal isn't the fastest movement. It's stable progress. 👉 Takeaway: When loss behaves unpredictably, learning rate should be one of the first things you investigate.

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❔Why Batch Normalization Makes Deep Networks Easier to Train Training a deep network is a bit like passing a message through twenty people. If each person changes the message slightly, by the end it's completely different. The same thing happens inside neural networks. As earlier layers update, the distribution of values reaching later layers keeps changing. Every layer has to constantly readjust. Batch Normalization reduces this problem by normalizing each mini-batch during training. That gives later layers a more stable input distribution. ✅ The result? • Faster convergence • Higher learning rates • Less sensitivity to initialization • Better training stability

✅ Before Building Any Model, Answer These 5 Questions A surprising number of ML projects fail before training even begins. Before writing a single line of code, answer these: 1. What decision will this prediction help someone make? 2. What data won't exist when this model is deployed? 3. What's the cost of a wrong prediction? 4. Which metric actually reflects that cost? 5. How will success be measured six months from now? Most modeling mistakes start here, not in the code.

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🔍 5 resources most ML people never stumble on These are the things practitioners quietly rely on but rarely share. 1. Google's "Rules of Machine Learning" 43 numbered rules from Google engineers on when to add complexity, how to catch training/serving skew, and when a heuristic beats a model. Written from real production postmortems. 2. Chip Huyen's ML Systems Design notes Free breakdown of how companies actually design ML systems: data pipelines, feature stores, serving latency, model monitoring. The stuff no ML course teaches. 3. Full Stack Deep Learning Free course built on one premise: training the model is the easy 20%. Covers deployment, cost tradeoffs, data labeling, and how models fail in production. 4. alphaXiv Same papers as arXiv, but with inline comment threads under each section, sometimes answered by the paper's own authors. Turns a static PDF into an ongoing discussion. 5. Sebastian Raschka's "Ahead of AI" Newsletter that dissects specific architecture and training decisions (why this optimizer, why this attention variant) at a depth most blogs skip.

Machine Learning Roadmap

Machine Learning Roadmap
|
|-- Fundamentals
|   |-- Mathematics
|   |   |-- Linear Algebra
|   |   |-- Calculus (Gradients, Optimization)
|   |   |-- Probability and Statistics
|   |   |-- Matrix Operations
|   |
|   |-- Programming
|   |   |-- Python (NumPy, Pandas, Scikit-learn)
|   |   |-- R (Optional for Statistical Modeling)
|   |   |-- SQL (For Data Extraction)
|
|-- Data Preprocessing
|   |-- Data Cleaning
|   |-- Feature Engineering
|   |   |-- Encoding Categorical Data
|   |   |-- Feature Scaling (Standardization, Normalization)
|   |   |-- Handling Missing Values
|   |-- Dimensionality Reduction (PCA, LDA)
|
|-- Supervised Learning
|   |-- Regression
|   |   |-- Linear Regression
|   |   |-- Polynomial Regression
|   |   |-- Ridge and Lasso Regression
|   |-- Classification
|   |   |-- Logistic Regression
|   |   |-- Decision Trees
|   |   |-- Support Vector Machines (SVM)
|   |   |-- Ensemble Methods (Random Forest, Gradient Boosting, XGBoost)
|
|-- Unsupervised Learning
|   |-- Clustering
|   |   |-- K-Means
|   |   |-- Hierarchical Clustering
|   |   |-- DBSCAN
|   |-- Dimensionality Reduction
|   |   |-- Principal Component Analysis (PCA)
|   |   |-- t-SNE
|   |-- Association Rules (Apriori, FP-Growth)
|
|-- Reinforcement Learning
|   |-- Markov Decision Processes
|   |-- Q-Learning
|   |-- Deep Q-Learning
|   |-- Policy Gradient Methods
|
|-- Model Evaluation and Optimization
|   |-- Train-Test Split and Cross-Validation
|   |-- Performance Metrics
|   |   |-- Accuracy, Precision, Recall, F1-Score
|   |   |-- ROC-AUC
|   |   |-- Mean Squared Error (MSE), R-squared
|   |-- Hyperparameter Tuning
|   |   |-- Grid Search
|   |   |-- Random Search
|   |   |-- Bayesian Optimization
|
|-- Deep Learning
|   |-- Neural Networks
|   |   |-- Perceptrons
|   |   |-- Backpropagation
|   |-- Convolutional Neural Networks (CNN)
|   |   |-- Image Classification
|   |   |-- Object Detection (YOLO, SSD)
|   |-- Recurrent Neural Networks (RNN)
|   |   |-- LSTM
|   |   |-- GRU
|   |-- Transformers (Attention Mechanisms, BERT, GPT)
|   |-- Tools and Frameworks (TensorFlow, PyTorch)
|
|-- Advanced Topics
|   |-- Transfer Learning
|   |-- Generative Adversarial Networks (GANs)
|   |-- Reinforcement Learning with Neural Networks
|   |-- Explainable AI (SHAP, LIME)
|
|-- Applications of Machine Learning
|   |-- Recommender Systems (Collaborative Filtering, Content-Based)
|   |-- Fraud Detection
|   |-- Sentiment Analysis
|   |-- Predictive Maintenance
|   |-- Autonomous Vehicles
|
|-- Deployment of Models
|   |-- Flask, FastAPI
|   |-- Cloud Deployment (AWS SageMaker, Azure ML)
|   |-- Containerization (Docker, Kubernetes)
|   |-- Model Monitoring and Retraining

Enjoy Learning 🙂

Check List to Become DataScientist

LLM Pipeline (How Large Language Models Generate Responses)