Many different types of 🤖 ML models, each with its own set of features and goals.

Notes

Understanding these models is important for designing and implementing effective systems.

Types

1. Supervised Learning: Trained on a dataset of labeled examples to make predictions or classifications based on input features.
2. Unsupervised Learning: Designed to learn from unlabeled data, used for clustering and reducing dimensions.
3. 🔄 Regression: Predicts continuous output variables based on input features.
4. 🏷 Classification: Predicts discrete output variables based on input features.
5. Clustering: Groups similar data together based on their features.
6. 🧠 Neural Network: Used for tasks like image recognition, natural language processing, and speech recognition.
7. 🏆 Reinforcement Learning: Learns to make decisions based on its environment, often used in games or robotics.
8. 🌀 Convolutional Neural Networks: Recognizes patterns in images for tasks like image recognition and facial recognition.
9. 🧠 Recurrent Neural Networks: Models sequences of data for NLP, speech recognition, and time series prediction.
10. Graphical Models: Represent complex data structures in graph-like fashion for tasks such as sentiment analysis and network analysis.
11. 🎨 Graph Convolutional Networks: Used to model graphs for social network analysis and recommender systems.
12. : A type of RNN used for NLP, speech recognition, and time series prediction.
13. 🌲 Hierarchical Neural Networks): Models hierarchical structures in data for NLP, sentiment analysis, and image recognition.
14. 🎭 Learning From Demonstration: Reinforcement learning where the model learns by observing its environment, used in robotics and autonomous vehicles.

Related

• 📚 LLM