Revolutionizing Startup Ranking: Neural Nets & Semantic Search
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In this riveting video from Connor Shorten, we dive headfirst into the world of Y Combinator startups, where Eric Jang's groundbreaking blog post takes center stage. Jang's ingenious project, YC Rank, shakes up the startup scene by using a neural net to rank these companies based on their execution complexity and mission success potential. Inspired by the project's unique dataset, Connor sets out on a quest to explore semantic search using the powerful Wev8 platform, showcasing how queries like "travel" or "cryptocurrency" can yield fascinating results without exact keyword matches.
With a deep dive into Eric's ranking model intricacies, Connor unpacks the process of training ranking architectures to assess the feasibility of startup ideas. The channel sheds light on the use of vector embeddings and cosine similarity in the ranking process, employing a modified RoBERTa classification head and essential regularization techniques due to the limited training data. Active learning and semi-supervised methods play a crucial role in enhancing ranking accuracy, as seen in the boosted test accuracy from 81% to a staggering 91% through active labeling.
As the video unfolds, Connor underscores the significance of data-centric AI in the realm of venture capital, lauding Eric's innovative approach of using company descriptions for startup ranking. The channel emphasizes the importance of sharing datasets through platforms like Hugging Face and Kaggle to propel advancements in data-centric AI. Through this exhilarating journey into the world of Y Combinator startups and neural net ranking, Connor Shorten ignites a spark of curiosity and innovation, showcasing the endless possibilities that lie at the intersection of technology and entrepreneurship.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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