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An AI model is a computer program. It learns from a large set of data. It finds patterns. It uses these patterns to make decisions on new data.
AI is the broad group. It includes machine learning and deep learning.
Machine learning is a part of AI. It uses math to learn from data. It does not need a human to program every rule.
Deep learning is a part of machine learning. It uses many layers of neural networks. It handles hard tasks like image and speech recognition.
Generative AI creates new content.
- Text models write articles and code.
- Image models make pictures.
- Audio models create music.
- Video models make clips.
- Multimodal models handle text and images at once.
- Reasoning models use logic for complex problems.
You build a model in four steps.
Training:
- Clean your data.
- Pick the right model.
- Feed data into the model.
- Adjust settings for better results.
Testing:
- Check accuracy.
- Measure precision and recall.
- Use a confusion matrix to see errors.
Deployment:
- Put the model in a real world setting.
Evaluation:
- Monitor for model decay.
- Watch for data drift. This happens when input data changes.
You also have pre-trained models. You fine-tune these with a small dataset. This adapts the model to your needs. Some models have bias from the original data. In those cases, you build from scratch.
Source: https://dev.to/ssenyonga_yasin_codes/what-is-an-ai-model-5e98 Optional learning community: https://t.me/GyaanSetuAi