Tabular

Is Tabular Editor a kind of magic wand that can help you transform data model “monsters” into “beauties” with a few clicks? Almost! Learn…Continue reading on Towards Data Science » Is Tabular Editor a kind of magic wand that can help you transform data model “monsters” into “beauties” with a few clicks? Almost! Learn…Continue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility!…

Making sense of out TabTransformer and learning to apply itPhoto by Samule Sun on UnsplashToday, Transformers are the key blocks in most of the state-of-the-art Natural Language Processing (NLP) and Computer Vision (CV) architectures. Nevertheless, tabular domain is still mainly dominated by gradient boosted decision trees (GBDT), so it was only logical that someone will attempt to bridge this gap. The first transformer-based models was introduced by Huang et al. (2020) in their TabTransformer: Tabular Data Modeling Using…

A la pixabay.Moving beyond classical conceptions of what tabular data can beDeep learning has become the public and private face of Artificial Intelligence. When one talks casually about Artificial Intelligence with friends at a party, strangers on the street, and colleagues at work, it is almost always on the exciting models which generate language, create art, synthesize music, and so on. Massive and intricately designed deep learning models power most of these exciting machine capabilities.Many practitioners, however,…

Introducing nbsynthetic: a simple but powerful tabular data generation open source library for small datasets.Image by author. Copyright NextBrain.mlIn this article we introduce nbsyntehtic, an open source project created by NextBrain.ml for a simple and robust unsupervised synthetic tabular data generation python library.Simple: Designed with a simple and stable unsupervised GAN (Generative Adversarial Network) architecture based in Keras.Robust: With specific hyperparameter tuning to ensure training stability while…

A series of mechanisms and tests that one can use to evaluate any tabular synthetic dataset, from resemblance and utility perspectivesPhoto by DeepMind on UnsplashSome of the main benefits of synthetic data are:Scalability: Whereas real training data is collected in a linear way, synthetic data can be generated in massive quantities, which alleviates the problem of needing to acquire a huge amount of high-quality training data for complex ML tasks.Privacy: Data availability remains a challenge due to complex privacy…

How to use SageMaker’s built-in algorithm TabTransformer with your own dataImage by author (created with OpenAI’s DALL·E 2)In June 2022 AWS announced a new range of built-in algorithms for Tabular Data. Since I’ve been working with Transformers in the NLP space for a while now, I was, of course, curious about the TabTransformer algorithm.The premise of the TabTransformer is promising: Categorical values in a feature can mean different things, depending on the context. Just like the word “bank” can mean different things in…