Artificial Intelligence

🔅 Machine Learning with Python: k-Means Clustering 📝 Learn the basics of k-means clustering, one of the most popular unsupervised machine learning approaches. 🌐 Author: Frederick Nwanganga 🔰 Level: Intermediate ⏰ Duration: 50m 📋 Topics: k-means clustering, Machine Learning, Python 🔗 Join Machine Learning for more courses

🔍 Machine Learning Cheat Sheet 🔍 1. Key Concepts: - Supervised Learning: Learn from labeled data (e.g., classification, regression). - Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction). - Reinforcement Learning: Learn by interacting with an environment to maximize reward. 2. Common Algorithms: - Linear Regression: Predict continuous values. - Logistic Regression: Binary classification. - Decision Trees: Simple, interpretable model for classification and regression. - Random Forests: Ensemble method for improved accuracy. - Support Vector Machines: Effective for high-dimensional spaces. - K-Nearest Neighbors: Instance-based learning for classification/regression. - K-Means: Clustering algorithm. - Principal Component Analysis(PCA) 3. Performance Metrics: - Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC. - Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R^2 Score. 4. Data Preprocessing: - Normalization: Scale features to a standard range. - Standardization: Transform features to have zero mean and unit variance. - Imputation: Handle missing data. - Encoding: Convert categorical data into numerical format. 5. Model Evaluation: - Cross-Validation: Ensure model generalization. - Train-Test Split: Divide data to evaluate model performance. 6. Libraries: - Python: Scikit-Learn, TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib. - R: caret, randomForest, e1071, ggplot2. 7. Tips for Success: - Feature Engineering: Enhance data quality and relevance. - Hyperparameter Tuning: Optimize model parameters (Grid Search, Random Search). - Model Interpretability: Use tools like SHAP and LIME. - Continuous Learning: Stay updated with the latest research and trends.

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🔅 Machine Learning with Python: Association Rules 📝 Explore the unsupervised machine learning approach known as association rules, as well as a step-by-step guide on how to use the approach for market basket analysis in Python. 🌐 Author: Frederick Nwanganga 🔰 Level: Intermediate ⏰ Duration: 1h 27m 📋 Topics: Machine Learning, Python 🔗 Join Machine Learning for more courses

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🔅 Machine Learning Foundations: Calculus 📝 Learn the basics of calculus concepts and techniques used to design and implement ML algorithms. 🌐 Author: Terezija Semenski 🔰 Level: Beginner ⏰ Duration: 1h 29m 📋 Topics: Calculus, Machine Learning 🔗 Join Machine Learning for more courses

Generating text is by no means a trivial task! LLMs are optimized to predict the probability of the next token, but how do we generate text with that? The naive approach is to use the probability vector generated by the model, choose the word with the highest probability, and autoregress. This is the greedy approach, but this tends to generate repetitive sentences that degenerate when they are too long. Another approach is to use the probabilities generated by the model and perform a sampling of the words based on those probabilities. Typically, we use a temperature parameter to adjust the level of randomness of this process. This allows to generate less repetitive and more creative sentences. But those 2 techniques have a problem. When we generate a sentence, we want to maximize the probability of the whole output sequence and not just the next token: P(Output sequence | Prompt) Fortunately, we can express this probability as a product of the probabilities to predict the next token: P(token 1, .., token N | Prompt) = P(token 1| Prompt) x ... P(token N |Prompt, token 1, ..., token N - 1) But solving this problem exactly is an NP-hard problem. So, instead, we can approximate the problem by choosing k candidate tokens at each iteration, testing them, and keeping the k sequences that maximize the probability of the whole sequence. In the end, we just choose the sequence with the highest probability. This is called the Beam search generation and can be mixed with the greedy and the multinomial approach. Another approach is the contrastive search, where we take into account additional metrics like fluency or diversity. At each iteration, we choose candidate tokens, penalize the probabilities with a similarity metric of the tokens that were previously generated, and choose the tokens that maximize the new score.