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ML Foundations for AI Engineers
- Intelligence boils down to understanding how the world works, requiring an internal model of the world for both humans and computers.
- Humans develop world models by learning from others and experiences, and computers learn similarly through machine learning.
- Traditional software development involves explicit instructions, while machine learning relies on curated examples for training models.
- Machine learning consists of training (learning from curated examples) and inference (applying the model to make predictions).
- Deep learning and reinforcement learning are special types of machine learning that enable computers to learn about the world.
- Deep learning involves training neural networks to learn optimal features for tasks, surpassing traditional model limitations.
- Training deep neural networks involves complex non-linearities and requires algorithms like gradient descent for parameter updates.
- Reinforcement learning allows models to learn through trial and error, with models improving based on rewards rather than explicit examples.
- Good data quality and quantity are crucial for training machine learning models, as bad data can hinder model performance.
- Machine learning provides a way for computers to align models to reality using data and mathematics, revolutionizing how tasks are learned and performed.
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