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Use Case / Category
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Use-Case
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AI/ML Models
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| Classification |
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Neural Networks
- Perceptron
- Multi-Layer Perceptron (MLP)
- Convolutional Neural Networks (CNN) - for image classification
- Recurrent Neural Networks (RNN) - for sequence classification
- Long Short Term Memory (LSTM) - for sequence classification
- Transformer Networks (like BERT, GPT-3) - for text classification
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| Regression (Prediction) |
Linear Regression, Polynomial Regression, Ridge Regression, Lasso Regression, Elastic Net |
| Clustering |
K-means, Hierarchical clustering, DBSCAN, Mean-shift |
| Dimensionality Reduction |
Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Autoencoders |
| Recommendation |
Collaborative Filtering, Content-Based Filtering, Hybrid recommendation systems |
| Anomaly Detection |
Isolation Forest, One-Class SVM, Local Outlier Factor (LOF) |
| Sequence Prediction |
Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU) |
| Natural Language Processing (NLP) |
Transformer Models (BERT, GPT-3, GPT-4), Seq2Seq Models, RNN and LSTM |
| Computer Vision |
Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), YOLO, SSD (for object detection) |
| Action/Control |
- Reinforcement Learning
- Q-Learning
- Deep Q-Networks (DQN)
- SARSA (State-Action-Reward-State-Action)
- Policy Gradients
- Actor-Critic methods (e.g., A2C, A3C)
- Proximal Policy Optimization (PPO)
- Deep Deterministic Policy Gradient (DDPG)
- Monte Carlo methods
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Reference
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