teras - a unified deep learning library for tabular data!#
Hello, world! Welcome to the documentation of teras.
teras (short for Tabular Keras) goal is to be your one stop for everything related to deep learning with tabular data and to accelerate tabular research.
Note
teras v0.3 is now fully based on Keras 3, making everything available backend agnostic. It supports TensorFlow, JAX and PyTorch backends.
teras provides state of the art layers, models and architectures for all purposes, be it classification, regression or even data generation and imputation using state of the art deep learning architectures.
It also includes functions and classes for preprocessing data for complex architectures, making it extremely simple to transform your data in the expected format, saving you loads of hassle and time!
While these state of the art architectures can be quite sophisticated, Teras, thanks to the incredible design of Keras, abstracts away all the complications and sophistication and makes it easy as ever to access those models and put them to use.
Not only that, everything available is highly customizable and modular, allowing for all variety of use cases.
Teras offers state of the art architectures as backbones for building customizable and modular models for classification and regression quickly and easily!
Generate synthetic dataset based on a small dataset or impute a dataset with missing features using the state of the art generative models offers by teras.
Setting up your data to be fed into the specialized architectures can be a challenge, but teras makes it super easy and intuitive by offering DataTransformer and DataSampler classes.
Installation#
>>> pip install teras