Deep dive: Knowledge distillation
In this deep dive seri, we are going over Knowledge distillation (KD), Partial Function Application, Learning rate Scheduler for LLM
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Concept
The concepts of a specific academic topic is discussed.
QLoRA: efficiently LLM Fine-Tuning
Parameter-efficient training (PEFT) techniques offer a way to fine-tune large language models (LLMs) on custom datasets with minimal computational resources.
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Stanford CS25 – Transformers United
Want to understand Language Models? Stanford CS25, titled “Transformers United,” is a comprehensive lecture series available on YouTube, presented by
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Build a Large Language Model (From Scratch)
“Build a Large Language Model (From Scratch)” by Sebastian Raschka blew up on Github this week and collected over 5000
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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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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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