AI Model - tech9tel/ai GitHub Wiki

AI Model

An AI model is a trained mathematical construct that can make predictions or decisions based on input data. It's the result of training an algorithm on a dataset, allowing it to recognize patterns and perform tasks like classification, translation, or generation.

🧠 What Is an AI Model?

A model is the trained version of an architecture, shaped by data and learning algorithms to perform tasks like:

  • Image classification (e.g., ResNet)
  • Text generation (e.g., GPT)
  • Translation (e.g., MarianMT)
  • Audio recognition (e.g., Whisper)

🧰 Model Components

  • Parameters – Values learned from training data (e.g., weights in neural networks).
  • Features – Inputs used to make predictions.
  • Loss Function – Measures how well the model performs.
  • Training & Inference – Learning from data vs making predictions.

πŸ” End-to-End Model Workflow in AI

  1. ** Define the Problem & Goal** 🧠🎯

    • What: Define what type of problem you are trying to solve & Also the objective clearly (e.g., classification, prediction, recommendation, regression).
    • Why: Understanding the problem helps in choosing the correct algorithm.
    • Example: If you want to classify emails as spam or not, it's a classification problem.

  2. Data Collection πŸ“¦πŸ“Š

    • What: Gather data from relevant sources (databases, APIs, sensors, user logs, etc.).
    • Why: The quality and quantity of data directly affect the model's performance.
    • Example: For predicting customer churn, collect data on customer behavior, subscription status, and demographics.

  3. Data Preprocessing 🧹

    • What: Clean and format the data (handle missing values, normalization, feature extraction, scaling).
    • Why: Raw data can have inconsistencies and noise that hinder the model’s learning.
    • Example: Removing outliers, normalizing values, or converting text data into numerical formats like one-hot encoding.

  4. Data Splitting βœ‚οΈ

    • Train / Validation / Test split for proper evaluation.

  5. Model Selection πŸ€–

    • What: Choose an appropriate machine learning algorithm or model architecture.(e.g., Decision Tree, CNN, Transformer).
    • Why: Different models work better for different types of problems. Choose based on the problem type and data available.
    • Example: If it's a classification problem, you might start with Logistic Regression, Decision Trees, or Neural Networks.

  6. Model Architecture Design πŸ—οΈ

    • Design or configure architecture (especially for DL models like CNN, RNN, Transformer).

  7. Model Training πŸ‹οΈβ€β™‚οΈπŸ“š

    • What: Train the model on the preprocessed data, so it can learn patterns and relationships.

    • Why: The model needs to learn from data to make accurate predictions or classifications.

    • Example: A neural network adjusts its weights to minimize error when predicting whether an email is spam.

    • Use the training data to fit the model using:

      • Forward Propagation
      • Loss Calculation
      • Backpropagation
      • Gradient Descent Optimization

  8. Model Evaluation πŸ“ˆπŸ“Š

    • What: Assess the model's performance using metrics like Accuracy, Precision, Recall, F1-Score, AUC to evaluate model performance.
    • Why: Evaluation helps determine if the model is learning effectively or if improvements are needed.
    • Example: Use cross-validation or test the model on unseen data to evaluate how well it generalizes.

  9. Hyperparameter Tuning βš™οΈπŸŽ›οΈ

    • Use Grid Search, Random Search, or Bayesian Optimization to improve results.
    • What: Fine-tune the model by adjusting hyperparameters (e.g., learning rate, batch size).
    • Why: Hyperparameter tuning can significantly improve model performance.
    • Example: For a neural network, adjusting the number of layers, activation functions, or learning rate can boost accuracy.

  10. Regularization Techniques πŸ›‘οΈ

    • Apply Dropout, Early Stopping, L1/L2 Regularization to prevent overfitting.

  11. Model Validation βœ…

    • Final testing on unseen data. Use techniques like Cross-Validation.

  12. Model Deployment* πŸš€

  • What: Deploy the trained model to a production environment so it can make real-time predictions. Deploy to cloud/server/mobile using APIs, containers, or model hubs.
  • Why: Deployment allows the model to be used in real-world applications, providing valuable insights or predictions.
  • Example: A recommendation system deployed on an e-commerce site that recommends products to users based on their browsing history.
  1. Monitoring & Feedback Loop πŸ“ˆ
  • What: Continuously track the model’s performance in production and retrain if necessary.
  • Why: Over time, models can degrade as new data becomes available. Monitoring ensures the model remains effective.
  • Example: If a recommendation model is no longer accurately predicting customer preferences, retrain it with updated data.

πŸ”„ Typical Tools Across Workflow

Stage Tools / Frameworks
Data Collection SQL, APIs, Web Scraping, Kafka
Preprocessing Pandas, NumPy, Scikit-learn
Modeling Scikit-learn, TensorFlow, PyTorch
Tuning Optuna, Ray Tune, Hyperopt
Deployment Flask, FastAPI, Docker, ONNX, Hugging Face
Monitoring Prometheus, Grafana, MLflow, Evidently AI

πŸ“š Pretraining & Tuning

πŸ” Type 🧠 What It Does
Pretrained Model Trained on generic data, reusable for various tasks
Fine-tuned Model Customized on specific data/task post pretraining
Instruction-tuned Model Fine-tuned to follow user commands (e.g., ChatGPT)
Zero-shot Model Can perform unseen tasks without training
Few-shot Model Learns from a few examples
Self-supervised Model Learns from data without manual labels

πŸ’¬ Language Models

🧾 Type 🧠 What It Does
LLM (Large Language Model) A model trained on massive text data to generate & understand language
VLLM (Very Large LLM) LLM with 100B+ parameters (e.g., GPT-4, Claude)
Causal Language Model Predicts next word/token in a sequence (e.g., GPT)
Masked Language Model Predicts missing words (e.g., BERT)

πŸ§ͺ Specialized Models

🧠 Type 🧠 What It Does
Generative Model Creates new content (text, image, etc.)
Discriminative Model Classifies input into categories
Multi-task Model Handles more than one task at a time
Multi-modal Model Processes multiple data types (text+image+audio)
Retrieval-Augmented Model Fetches data from external sources during inference (e.g., RAG)

πŸ“‚ Source & Licensing

πŸ”“ Type 🧠 What It Means
Open-Source Model Weights/code are publicly available (e.g., LLaMA, Falcon)
Closed-Source Model Proprietary and not openly accessible (e.g., GPT-4, Gemini)

🧠 AI Models – Grouped by Use Case

πŸ“ 1. Language Models (NLP)

  • GPT – Generative Pre-trained Transformer for text generation
  • BERT – Bidirectional contextual model for language understanding
  • T5 – Text-to-Text Transfer Transformer, unifies NLP tasks into text format
  • XLNet – Autoregressive pretraining with permutation-based modeling
  • LLaMA / PaLM / Gemini – Modern LLMs with open-source or proprietary access

πŸ–ΌοΈ 2. Vision Models (Computer Vision)

  • CNN – Convolutional Neural Network, base model for image tasks
  • ResNet – Residual Network with skip connections
  • EfficientNet – Parameter-optimized CNNs
  • YOLO – Real-time object detection model
  • Vision Transformer (ViT) – Transformer applied to image patches

πŸ”Š 3. Speech & Audio Models

  • Whisper – Speech-to-text model by OpenAI
  • Wav2Vec2 – Self-supervised learning for speech recognition
  • Tacotron – Text-to-speech synthesis
  • DeepSpeech – End-to-end speech recognition model by Mozilla
  • Conformer – CNN + Transformer hybrid for audio

🎨 4. Generative Models

  • GAN – Generative Adversarial Network, uses two competing networks
  • VAE – Variational Autoencoder for probabilistic generation
  • Diffusion Models – Used for realistic image/text/audio synthesis (e.g., Stable Diffusion)
  • StyleGAN – High-quality image synthesis
  • PixelCNN – Autoregressive image generation

πŸ•ΉοΈ 5. Reinforcement Learning Models

  • DQN – Deep Q-Network
  • PPO – Proximal Policy Optimization for stable learning
  • A3C – Asynchronous Advantage Actor-Critic
  • MuZero – Model-based learning without known environment rules
  • AlphaZero – Self-learning system for strategy games

πŸ” 6. Sequence & Time-Series Models

  • RNN – Recurrent Neural Network for sequences
  • LSTM – Long Short-Term Memory for long sequences
  • GRU – Gated Recurrent Unit, a simpler alternative to LSTM
  • Transformer – Attention-based sequence model
  • TCNs – Temporal Convolutional Networks for ordered data

🧩 7. Foundation & Multimodal Models

  • CLIP – Connects vision and language
  • Flamingo / Gemini – Multimodal large models (text, vision, audio)
  • SAM – Segment Anything Model by Meta
  • DALLΒ·E – Text-to-image generative model
  • GPT-4V – Multimodal extension of GPT-4