Curated insights and tools for curious minds.
📅 Date: 28/02/2025
⏱️Read time: 3 minutes
This week I have been thinking and researching potential implementation of generative AI into my computer vision related research. Therefore, I found the below listed review articles significant.
📰 Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives – This paper is great for beginners level who wants to learn not just about the recently emerging architecture but also their uses, challenges during their implementation, plus opinions on general ecosystem including community initiatives, diminishing inclination in openly sharing research findings, relevant technology advances, datasets availability and potential future directions such as inclusive and ethical LLMs.
Reference: Hagos, D. H., Battle, R., & Rawat, D. B. (2024). Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives. IEEE Transactions on Artificial Intelligence. https://doi.org/10.1109/TAI.2024.3444742
🔧 LLM Visualisation: This teaching-by-visualizing tool lets you observe what occurs in the background when LLMs are working. It offers multiple architectures to inspect.
Source Link https://bbycroft.net/llm
🛠️ Deep Learning Course by Andrew NG: Andreas Horn´s post highlights Andrew Ng’s Deep Learning course on Coursera as an essential resource for mastering AI and deep learning. It shares a cheatsheet summarizing key topics, including neural networks, hyperparameter tuning, optimization, CNNs, and sequence models. The course provides a deep understanding of deep learning concepts beyond just surface-level explanations. Notes compiled by Mahmoud Badry are available for download.
Valuable content from LinkedIn or other platforms.
💬 Top 40 Websites to Download Research Papers for Free: If you are new researcher like me, here is an open access resources to find your next paper: