Direct Preference Optimization instead of RLHF
Can you still do cutting-edge research on LLM if you do not have massive compute resources? RLHF became a key
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Concept
The concepts of a specific academic topic is discussed.
LLM course in notebook
The “Large Language Model Course” blew up on Github this week and collected over 9000 stars. It’s a course on
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Generative Multimodal Models are In-Context Learners
The main objectives of this work include demonstrating that large multimodal models can enhance their task-agnostic in-context learning capabilities through
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8 Microsoft free Github Courses
In this article, we are going to explore 8 different Microsoft Github hosted courses for machine learning and AI. You
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Normalization vs Standardization: When to use which
Feature engineering plays a vital role in creating precise and efficient machine learning models. A crucial component of this process
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Machine learning: a quick review (part 7)
7- Bayesian Learning Bayesian statistics provides us the tools to update our beliefs (represented as probability distributions) based on new
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Machine learning: a quick review (part 6)
6- Ensemble techniques 6-1- What are ensemble techniques In ensemble learning, you take multiple algorithms or same algorithm multiple times
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Machine learning: a quick review (part 5)
5- Not supervised learning 5-1- Unsupervised learning unsupervised learning is a technique that determines patterns and associations in unlabeled data.
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Machine learning: a quick review (part 4)
4- Supervised learning 4-1- Decision tree models partition the feature space into rectangles and learn a simple (e.g. constant) model
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Machine learning: a quick review (part 3)
3- Data preparation 3-1- Imbalanced dataset An imbalanced dataset is when you have, for example, a classification test and 90%
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