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How does supervised learning close the gap between predictions and actual outcomes?
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Supervised learning uses labeled data to compare predictions against real outcomes, adjusting the model to minimize errors and improve accuracy.
Give an example of how unsupervised learning can be applied.
Unsupervised learning can be used to group employees based on tenure and income data without predefined labels.
What is semi-supervised learning and give an example?
Semi-supervised learning combines small labeled datasets with large unlabeled datasets; for example, using it for fraud detection in banking.
What are discriminative models and generative models?
Discriminative models classify data points based on labels, while generative models generate new data based on patterns in training data.
Describe an example that differentiates discriminative and generative models.
A discriminative model might classify images as cats or dogs, while a generative model could create new images of dogs based on existing data patterns.
What is the benefit of understanding basic AI concepts for the use of AI tools?
Understanding basic AI concepts enhances the practical use of AI tools by providing foundational knowledge essential for effectively leveraging these technologies.
Why are neural networks used in deep learning?
Neural networks, inspired by the human brain, consist of layers of nodes (neurons) and allow deep learning models to become more powerful with more layers.
What distinguishes deep learning from other types of machine learning?
Deep learning uses artificial neural networks with multiple layers of nodes, allowing for the processing of complex data and the generation of more powerful models.
What is a Large Language Model (LLM) and what are its stages of training?
An LLM is a deep learning model subset, and it involves two stages: pre-training on general language tasks and fine-tuning on specialized tasks in various industries.
What impacts do large datasets have on the efficacy of deep learning models?
Large datasets enhance the learning capability and accuracy of deep learning models by providing extensive information for training the neural networks.
What is the purpose of pre-training in LLMs?
Pre-training in LLMs involves training the model on general language tasks to provide a broad understanding of language before fine-tuning for specific tasks.
How does supervised learning differ from unsupervised learning in machine learning?
Supervised learning uses labeled data and compares predictions to training data, while unsupervised learning uses unlabeled data without predefined labels.
What are the different applications of generative AI?
Generative AI can be used in text-to-text (ChatGPT, Google Bard), text-to-image (MidJourney, DALL-E), text-to-video (Imagen Video, Make-A-Video), text-to-3D (Shap-E Model), and text-to-task models (summarizing emails).
How can LLMs be economically advantageous for different institutions?
Big tech companies develop general LLMs, while smaller institutions fine-tune them with domain-specific data, thus benefiting from tailored solutions without the high cost of initial model development.
Can you name some text-to-video generative AI models?
Some text-to-video generative AI models include Google's Imagen Video, CogVideo, and Meta’s Make-A-Video.
What is the relationship between AI, Machine Learning (ML), and Deep Learning?
AI is a broad field like physics, ML is a subfield of AI like thermodynamics, and Deep Learning is a subset of ML.
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