Difference between revisions of "Learning Techniques"
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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 | ||
| − | * [[In-Context Learning (ICL)]] ... [[Large Language Model (LLM)|LLM]]s understand to encode learning algorithms implicitly during their training processes | + | * [[In-Context Learning (ICL)]] ... [[Large Language Model (LLM)|LLM]]s understand to encode learning algorithms implicitly during their training processes ... [[Context]] |
* [[PRIMO.ai#Supervised|Supervised Learning]] | * [[PRIMO.ai#Supervised|Supervised Learning]] | ||
** [https://en.wikipedia.org/wiki/Deductive_classifier Deductive Inference | Wikipedia] | ** [https://en.wikipedia.org/wiki/Deductive_classifier Deductive Inference | Wikipedia] | ||
Revision as of 20:37, 17 May 2023
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- Artificial Intelligence (AI) ... Machine Learning (ML) ... Deep Learning ... Neural Network ...Learning Techniques
- 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery
- Multimodal Machine Learning ... Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
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 ... Context
- 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
