When training a machine learning model, you may sometimes work with datasets with a large number of features. However, only a small subset of these features will actually be important for the model to make predictions. Which is why you need feature selection to identify these helpful features. This article covers useful tips for feature …
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Machine learning projects often require the execution of a sequence of data preprocessing steps followed by a learning algorithm. Managing these steps individually can be cumbersome and error-prone. This is where sklearn pipelines come into play. This post will explore how pipelines automate critical aspects of machine learning workflows, such as data preprocessing, feature engineering, …
When I was in high school and studied complex mathematics problems, I always used to think about why we were studying them or why they were useful. I was unable to understand and find their usage in the real world. Since machine learning is also a trending topic that many people want to explore, the …
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At its core, Stable Diffusion is a deep learning model that can generate pictures. Together with some other models and UI, you can consider that as a tool to help you create pictures in a new dimension that not only you can provide instructions on how the picture looks like, but also the generative model …
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Predictive modeling in finance uses historical data to forecast future trends and outcomes. R, a powerful statistical programming language, provides a robust set of tools and libraries for financial analysis and modeling. This article explores the key techniques and packages in R that are commonly used for predictive modeling in finance. We’ll cover time series …
In recent years, machine learning has experienced a profound transformation with the emergence of LLMs and new techniques that improved the domain’s state of the art. Most of these advancements have mainly been initially revealed in research papers, which have introduced new techniques while reshaping our understanding and approach to the domain. The number of …
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Reinforcement Learning (RL) has emerged as a powerful paradigm in artificial intelligence, enabling machines to learn optimal behavior through interaction with their environment. In RL, an agent learns to make decisions by performing actions and receiving rewards or penalties, ultimately aiming to maximize cumulative rewards over time. This approach has led to remarkable advancements across …
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When we began studying jailbreak evaluations, we found a fascinating paper claiming that you could jailbreak frontier LLMs simply by translating forbidden prompts into obscure languages. Excited by this result, we attempted to reproduce it and found something unexpected.
Hugging Face has significantly contributed to the breakthrough of machine learning application technology, especially in the NLP field. They could contribute a lot because Hugging Face focuses on building a platform for the community to easily access models, tools, and datasets to the public. That’s why Hugging Face has become a place to contribute to …
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As we progress through 2024, machine learning (ML) continues to evolve at a rapid pace. Python, with its rich ecosystem of libraries, remains at the forefront of ML development. In this post, we’ll explore the top 10 Python libraries dominating the ML scene in 2024, how the field has changed since 2020, and the key …
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