From 787ac00d2cc6e6bfba6d891e4812710cd4b2531b Mon Sep 17 00:00:00 2001 From: medusa Date: Fri, 26 Apr 2024 11:15:30 +0000 Subject: [PATCH] Update docs/tech_docs/llm/ai_over_view.md --- docs/tech_docs/llm/ai_over_view.md | 63 +++++++++++++++++++++++++++++- 1 file changed, 61 insertions(+), 2 deletions(-) diff --git a/docs/tech_docs/llm/ai_over_view.md b/docs/tech_docs/llm/ai_over_view.md index 4571be0..3b5a45c 100644 --- a/docs/tech_docs/llm/ai_over_view.md +++ b/docs/tech_docs/llm/ai_over_view.md @@ -51,6 +51,65 @@ Computer Vision focuses on enabling computers to interpret and understand visual Popular computer vision libraries include OpenCV, scikit-image, and TensorFlow's object detection API. -This technical deep-dive provides an overview of the key concepts, techniques, and libraries in Generative AI, Natural Language Processing, Deep Learning, and Computer Vision. Each of these topics is vast and rapidly evolving, with numerous subtopics and advanced techniques. +## 5. Machine Learning -To dive deeper into any specific area, I recommend exploring research papers, online courses, and practical projects that align with your interests. Feel free to ask for more detailed explanations or examples on any particular subtopic within these domains. \ No newline at end of file +Machine Learning (ML) is a subset of AI that focuses on enabling computers to learn and improve from experience without being explicitly programmed. The main types of machine learning are: + +- Supervised Learning: The model learns from labeled data, where each input has a corresponding output or target variable. Examples include regression and classification tasks. +- Unsupervised Learning: The model learns from unlabeled data, discovering hidden patterns or structures on its own. Examples include clustering and dimensionality reduction. +- Reinforcement Learning: The model learns through interaction with an environment, receiving rewards or penalties for its actions, and learning to maximize the cumulative reward. + +Popular machine learning libraries include scikit-learn, TensorFlow, and PyTorch. + +## 6. Recommender Systems + +Recommender Systems are AI algorithms that provide personalized recommendations to users based on their preferences, behavior, and historical data. The main types of recommender systems are: + +- Content-based Filtering: Recommendations are based on the characteristics or features of items that a user has liked in the past. +- Collaborative Filtering: Recommendations are based on the preferences and behavior of similar users. +- Hybrid Approaches: Combining content-based and collaborative filtering techniques to improve recommendation accuracy. + +Popular libraries for building recommender systems include Surprise, LightFM, and TensorFlow Recommenders. + +## 7. Time Series Analysis + +Time Series Analysis involves analyzing and modeling data points collected over time to extract meaningful insights, make predictions, or detect anomalies. Key concepts and techniques in time series analysis include: + +- Time Series Decomposition: Breaking down a time series into its constituent components, such as trend, seasonality, and residuals. +- Autoregressive Models: Models that predict future values based on a linear combination of past values (e.g., AR, ARMA, ARIMA). +- Exponential Smoothing: Techniques that assign exponentially decreasing weights to past observations for forecasting (e.g., Holt-Winters). +- Deep Learning for Time Series: Using deep learning models like LSTMs, GRUs, or Temporal Convolutional Networks (TCNs) for time series forecasting. + +Popular libraries for time series analysis include statsmodels, Prophet, and TensorFlow Time Series. + +## 8. Anomaly Detection + +Anomaly Detection is the process of identifying rare or unusual data points, patterns, or events that deviate significantly from the norm. Anomaly detection techniques can be classified into: + +- Statistical Methods: Identifying data points that fall outside a predefined statistical range or distribution. +- Machine Learning Methods: Using supervised, unsupervised, or semi-supervised learning algorithms to learn normal patterns and detect deviations. +- Deep Learning Methods: Utilizing deep neural networks, such as Autoencoders or Generative Adversarial Networks (GANs), to learn complex representations of normal data and identify anomalies. + +Popular libraries for anomaly detection include PyOD, scikit-learn, and TensorFlow. + +## 9. Model Evaluation and Selection + +Model Evaluation and Selection involves assessing the performance of trained models and choosing the best one for a given task. Key concepts and techniques include: + +- Evaluation Metrics: Quantitative measures to assess model performance, such as accuracy, precision, recall, F1-score, mean squared error (MSE), or mean absolute error (MAE). +- Cross-Validation: Techniques like k-fold cross-validation or stratified k-fold to assess model performance on multiple subsets of the data. +- Hyperparameter Tuning: Searching for the optimal combination of hyperparameters that maximizes model performance using techniques like grid search, random search, or Bayesian optimization. +- Model Comparison: Comparing the performance of different models or algorithms using statistical tests or visualization techniques. + +Popular libraries for model evaluation and selection include scikit-learn, Hyperopt, and Optuna. + +## 10. Explainable AI (XAI) + +Explainable AI (XAI) focuses on developing techniques and methods to make AI models more transparent, interpretable, and understandable to humans. Key concepts and techniques in XAI include: + +- Feature Importance: Identifying the most influential features or variables that contribute to a model's predictions. +- SHAP (SHapley Additive exPlanations): A game-theoretic approach to explain individual predictions by assigning importance values to each feature. +- LIME (Local Interpretable Model-agnostic Explanations): A technique that approximates a complex model with a simpler, interpretable model locally around a specific prediction. +- Counterfactual Explanations: Generating hypothetical examples that show how changes in input features would affect the model's predictions. + +Popular libraries for explainable AI include SHAP, LIME, and AIX360. \ No newline at end of file