diff --git a/docs/tech_docs/llm/ai_over_view.md b/docs/tech_docs/llm/ai_over_view.md index 3b5a45c..59b6de7 100644 --- a/docs/tech_docs/llm/ai_over_view.md +++ b/docs/tech_docs/llm/ai_over_view.md @@ -1,115 +1,184 @@ -# Technical Deep-Dive: AI Fundamentals +Certainly! Here's the updated structure of the AI fundamentals documentation based on your requirements: -## 1. Generative AI +# AI Fundamentals: A Technical Overview -Generative AI focuses on creating new content, such as images, text, or audio, using deep learning models. The most popular generative models include: +## 1. Machine Learning -- Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, that compete against each other. The generator learns to create realistic content, while the discriminator learns to distinguish between real and generated content. -- Variational Autoencoders (VAEs): VAEs are probabilistic models that learn to encode input data into a lower-dimensional latent space and then decode it back to the original space, enabling the generation of new samples. -- Transformer-based Models: Models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) have shown remarkable success in generating coherent and contextualized text. +### Summary +Machine Learning enables computers to learn and improve from experience without being explicitly programmed. It involves training models on data to make predictions or discover patterns. -Key techniques in Generative AI include adversarial training, latent space manipulation, and attention mechanisms. +### Key Topics +- Supervised Learning +- Unsupervised Learning +- Reinforcement Learning +- Feature Engineering +- Model Selection and Training +- Evaluation Metrics -## 2. Natural Language Processing (NLP) +### Key Real-World Applications +- Predictive Analytics +- Fraud Detection +- Recommendation Systems +- Customer Segmentation +- Anomaly Detection -NLP deals with the interaction between computers and human language. It involves various tasks, such as: +### Emerging Topics in Machine Learning +- Federated Learning +- AutoML +- Interpretable Machine Learning +- Quantum Machine Learning -- Text Preprocessing: Tokenization, stemming, lemmatization, and removing stop words. -- Word Embeddings: Representing words as dense vectors to capture semantic relationships (e.g., Word2Vec, GloVe). -- Sequence Modeling: Capturing sequential dependencies using models like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. -- Attention Mechanisms: Allowing models to focus on relevant parts of the input (e.g., self-attention in Transformers). -- Named Entity Recognition (NER): Identifying and classifying named entities in text. -- Sentiment Analysis: Determining the sentiment or emotion expressed in text. -- Machine Translation: Translating text from one language to another. +## 2. Deep Learning -Popular NLP libraries include NLTK, spaCy, and Hugging Face's Transformers. +### Summary +Deep Learning involves training artificial neural networks with multiple layers to learn hierarchical representations from data. It has revolutionized various domains, including computer vision, natural language processing, and speech recognition. -## 3. Deep Learning +### Key Topics +- Neural Network Architectures (Feedforward, CNNs, RNNs, Autoencoders) +- Optimization Algorithms +- Regularization Techniques +- Transfer Learning +- Generative Models (VAEs, GANs) -Deep Learning involves training artificial neural networks with multiple layers to learn hierarchical representations from data. Key concepts and architectures in deep learning include: +### Key Real-World Applications +- Image and Video Recognition +- Natural Language Understanding +- Speech Synthesis and Recognition +- Autonomous Vehicles +- Medical Diagnosis -- Feedforward Neural Networks: The simplest type of neural network where information flows in one direction from input to output. -- Convolutional Neural Networks (CNNs): Designed to process grid-like data (e.g., images), CNNs use convolutional layers to learn local patterns and hierarchical features. -- Recurrent Neural Networks (RNNs): Designed to handle sequential data, RNNs have feedback connections that allow information to persist across time steps. -- Autoencoders: Neural networks that learn to compress and reconstruct input data, often used for dimensionality reduction and feature learning. -- Regularization Techniques: Methods like L1/L2 regularization, dropout, and early stopping to prevent overfitting. -- Optimization Algorithms: Techniques like Stochastic Gradient Descent (SGD), Adam, and RMSprop for training neural networks efficiently. +### Emerging Topics in Deep Learning +- Unsupervised Representation Learning +- Self-Supervised Learning +- Neural Architecture Search +- Explainable Deep Learning -Popular deep learning frameworks include TensorFlow, PyTorch, and Keras. +## 3. Natural Language Processing (NLP) + +### Summary +Natural Language Processing enables computers to understand, interpret, and generate human language. It deals with the interaction between computers and human language in the form of text or speech. + +### Key Topics +- Text Preprocessing +- Word Embeddings +- Sequence Modeling +- Named Entity Recognition +- Sentiment Analysis +- Machine Translation + +### Key Real-World Applications +- Chatbots and Virtual Assistants +- Sentiment Analysis for Social Media +- Language Translation +- Text Summarization +- Information Extraction + +### Emerging Topics in NLP +- Transformer-based Models (BERT, GPT) +- Few-Shot Learning for NLP +- Cross-Lingual Language Models +- Multimodal NLP ## 4. Computer Vision -Computer Vision focuses on enabling computers to interpret and understand visual information from images and videos. Key tasks and techniques in computer vision include: +### Summary +Computer Vision enables computers to interpret and understand visual information from images and videos. It aims to replicate human vision and perception using artificial intelligence techniques. -- Image Preprocessing: Resizing, normalization, and data augmentation techniques. -- Feature Extraction: Techniques like edge detection, corner detection, and scale-invariant feature transform (SIFT) to extract meaningful features from images. -- Object Detection: Identifying and localizing objects within an image using models like YOLO (You Only Look Once), Faster R-CNN, and SSD (Single Shot MultiBox Detector). -- Semantic Segmentation: Assigning a class label to each pixel in an image, enabling precise object boundaries and scene understanding. -- Image Classification: Assigning a class label to an entire image using deep learning models like CNNs (e.g., ResNet, Inception, VGGNet). -- Optical Flow: Estimating the motion of objects between consecutive frames in a video. -- Pose Estimation: Detecting and tracking the location and orientation of objects or body parts in images or videos. +### Key Topics +- Image Preprocessing +- Feature Extraction +- Object Detection +- Semantic Segmentation +- Image Classification +- Optical Flow -Popular computer vision libraries include OpenCV, scikit-image, and TensorFlow's object detection API. +### Key Real-World Applications +- Facial Recognition +- Autonomous Vehicles +- Medical Image Analysis +- Surveillance and Security +- Augmented Reality -## 5. Machine Learning +### Emerging Topics in Computer Vision +- Unsupervised Visual Representation Learning +- Few-Shot Object Detection +- Adversarial Attacks and Defenses +- 3D Computer Vision -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: +## 5. Generative AI -- 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. +### Summary +Generative AI focuses on creating new content, such as images, text, or audio, using deep learning models. It enables computers to generate novel and realistic data samples. -Popular machine learning libraries include scikit-learn, TensorFlow, and PyTorch. +### Key Topics +- Generative Adversarial Networks (GANs) +- Variational Autoencoders (VAEs) +- Transformer-based Generative Models (GPT, BERT) +- Latent Space Manipulation +- Style Transfer -## 6. Recommender Systems +### Key Real-World Applications +- Image and Video Synthesis +- Text Generation +- Music and Audio Synthesis +- Virtual and Augmented Reality +- Design and Creative Industries -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: +### Emerging Topics in Generative AI +- Controllable Generation +- Multimodal Generation +- Disentangled Representation Learning +- Efficient Generative Models -- 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. +## 6. Model Evaluation and Selection -Popular libraries for building recommender systems include Surprise, LightFM, and TensorFlow Recommenders. +### Summary +Model Evaluation and Selection involves assessing the performance of trained models and choosing the best one for a given task. It ensures that models are reliable, accurate, and suitable for deployment. -## 7. Time Series Analysis +### Key Topics +- Evaluation Metrics (Accuracy, Precision, Recall, F1-Score, MSE, MAE) +- Cross-Validation +- Hyperparameter Tuning +- Model Comparison +- Bias-Variance Tradeoff -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: +### Key Real-World Applications +- Model Performance Assessment +- Model Selection for Production +- Hyperparameter Optimization +- Model Interpretability and Fairness -- 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. +### Emerging Topics in Model Evaluation and Selection +- Automated Machine Learning (AutoML) +- Bayesian Optimization for Hyperparameter Tuning +- Multi-Objective Optimization +- Ensemble Learning -Popular libraries for time series analysis include statsmodels, Prophet, and TensorFlow Time Series. +## 7. Explainable AI (XAI) -## 8. Anomaly Detection +### Summary +Explainable AI focuses on developing techniques to make AI models more transparent, interpretable, and understandable. It aims to provide insights into how models make decisions and to build trust in AI systems. -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: +### Key Topics +- Feature Importance +- SHAP (SHapley Additive exPlanations) +- LIME (Local Interpretable Model-agnostic Explanations) +- Counterfactual Explanations +- Rule-Based Explanations -- 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. +### Key Real-World Applications +- Explaining Decisions in Healthcare +- Fairness and Bias Detection +- Debugging and Improving Models +- Regulatory Compliance +- Building Trust in AI Systems -Popular libraries for anomaly detection include PyOD, scikit-learn, and TensorFlow. +### Emerging Topics in Explainable AI +- Causal Inference for Explanations +- Interpretable Deep Learning +- Adversarial Attacks on Explanations +- Human-Centered Explainable AI -## 9. Model Evaluation and Selection +This updated structure provides a comprehensive overview of each AI fundamental topic, including a summary, key topics, real-world applications, and emerging areas of research. It offers a clear and organized way to explore the different aspects of AI and dive deeper into specific areas of interest. -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 +Feel free to use this as a starting point for your AI learning journey and refer to the provided topics and applications as a guide for further exploration and practical implementation. \ No newline at end of file