Painting the cosmos of your digital dreams.
Leveraging Pre-trained Models, Augmenting Images and Bounding Boxes, and Unveiling the Power of Convolutional Neural Networks in Object… Continue reading on Towards Data Science »
How I Won a $10,000 Essay Writing Contest Communication in Data Science Recently I was lucky enough to win an essay writing contest on Kaggle, and have my work published in their 2023 AI report and while I definitely don’t see myself
Even fewer lines, and faster execution time, and some NLP Continue reading on Towards Data Science »
Product strategies from Classical ML to adapt (or ditch) for the generative AI world Image source: Tome Years ago, the first piece of advice my boss at Opendoor gave me was succinct: “Invest in backtesting. AI product teams succeed or fail based
Disruptively testing parts of neural networks and other ML architectures to make them more robust (Image generated by author using DALL-E). Interesting what AI thinks of it’s own brain. In a similar fashion to how a person’s intellect can be stress tested,
Tuning-Free Longer Context Lengths For LLMs — A Review of Self-Extend (LLM Maybe LongLM) A simple strategy to enable LLMs to consume longer context length inputs during inference without the need for finetuning. Source: Image created by the author using DALL-E 3 In this
Python Water Quality — Baseline Classification Model Assess feature importance when estimating water quality using a reference baseline model Photo by Unseen Studio on Unsplash Understanding what can be used to classify the water quality can be a challenge. Having expert knowledge of different
An approach to add conditions to CVAE models without retraining Requirements This article is about conditional variational autoencoders (CVAE) and requires a minimal understanding of this type of model. If you are not familiar with CVAEs, I can recommend the
A step-by-step guide on deploying LlamaIndex RAGs to AWS ECS fargate Continue reading on Towards Data Science »
Public domain/Public domain/Image by Author/Public domain/Public domain/CC BY-SA 4.0 Its definition, its many applications, its deep connection with inverse probability, and a glimpse at its history For all its heft, the Central Limit Theorem has a singularly succinct definition. It says, simply,
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