Difference between revisions of "Learning Techniques"
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* [https://machinelearningmastery.com/types-of-learning-in-machine-learning/ 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery] | * [https://machinelearningmastery.com/types-of-learning-in-machine-learning/ 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery] | ||
* [[PRIMO.ai#Natural Language|Natural Language]] | * [[PRIMO.ai#Natural Language|Natural Language]] | ||
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<b>Core:</b> four main types of learning problems in machine learning | <b>Core:</b> four main types of learning problems in machine learning | ||
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Machine learning (ML) is a field devoted to understanding and building methods that let machines "learn" – that is, methods that leverage data to improve computer performance on some set of tasks. It is seen as a broad subfield of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. - [https://en.wikipedia.org/wiki/Machine_learning#History_and_relationships_to_other_fields Machine learning | Wikipedia] | Machine learning (ML) is a field devoted to understanding and building methods that let machines "learn" – that is, methods that leverage data to improve computer performance on some set of tasks. It is seen as a broad subfield of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. - [https://en.wikipedia.org/wiki/Machine_learning#History_and_relationships_to_other_fields Machine learning | Wikipedia] | ||
Revision as of 11:31, 28 April 2023
YouTube search... ...Google search
- 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery
- Natural Language
- Machine Learning
Core: four main types of learning problems in machine learning
- In-Context Learning (ICL) ... LLMs understand to encode learning algorithms implicitly during their training processes
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning (RL)
Hybrid: drawing from unsupervised and supervised learning
Other Techniques:
- Deep Learning
- Transfer Learning a model trained on one task is re-purposed on a second related task
- Few Shot Learning
- Ensemble Learning
- Multi-Task Learning (MTL)
- Apprenticeship Learning - Inverse Reinforcement Learning (IRL)
- Imitation Learning
- Simulated Environment Learning
- Lifelong Learning - Catastrophic Forgetting Challenge
- Neural Structured Learning (NSL)
- Meta-Learning
- Online Learning
- Human-in-the-Loop (HITL) Learning / Active Learning
- Decentralized: Federated & Distributed Learning
Machine learning (ML) is a field devoted to understanding and building methods that let machines "learn" – that is, methods that leverage data to improve computer performance on some set of tasks. It is seen as a broad subfield of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. - Machine learning | Wikipedia
