Google’s AI Course for Beginners (in 10 minutes)!
Jeff Su・2 minutes read
Artificial intelligence encompasses machine learning and deep learning, with large language models (LLMs) playing a significant role in deep learning applications across various industries. LLMs are pre-trained on extensive datasets and fine-tuned for specific tasks, enhancing diagnostic accuracy in fields like healthcare and finance.
Insights
- Deep learning models can be discriminative or generative, with large language models (LLMs) falling under deep learning, showcasing the versatility and complexity of artificial intelligence subfields.
- Large language models, pre-trained with extensive data sets and fine-tuned for specific tasks, offer significant benefits to industries like healthcare and finance by enhancing diagnostic accuracy, highlighting the practical applications and impact of AI advancements.
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Recent questions
What is artificial intelligence?
Study of machine learning and deep learning.
What are large language models (LLMs)?
Subset of deep learning models.
How does machine learning work?
Trains models with input data for predictions.
What is the difference between discriminative and generative models?
Discriminative classifies data, generative creates new content.
How do large language models benefit industries like healthcare and finance?
Improve diagnostic accuracy through pre-training and fine-tuning.
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Summary
00:00
"AI, ML, DL: Learning Models in Technology"
- Artificial intelligence is a field of study, with machine learning being a subfield of AI, and deep learning a subset of machine learning.
- Deep learning models can be discriminative or generative, with large language models (LLMs) falling under deep learning.
- Machine learning involves training models with input data to make predictions, with supervised models using labeled data and unsupervised models using unlabeled data.
- Deep learning utilizes artificial neural networks inspired by the human brain, allowing for semi-supervised learning with a small amount of labeled data and a large amount of unlabeled data.
- Generative AI models learn patterns in training data to generate new content, while discriminative models classify data points based on labels.
- Large language models are pre-trained with vast data sets and then fine-tuned for specific purposes, benefiting industries like healthcare and finance by improving diagnostic accuracy.
