What is baikal?
It is a graph-based, functional API for building complex machine learning pipelines of objects that implement the scikit-learn API. It is mostly inspired on the excellent Keras API for Deep Learning, and borrows a few concepts from the TensorFlow framework and the (perhaps lesser known) graphkit package. It aims to provide an API that allows to build complex, non-linear machine learning pipelines.
baikal is a tool in the Machine Learning Tools category of a tech stack.
baikal is an open source tool with 570 GitHub stars and 28 GitHub forks. Here’s a link to baikal's open source repository on GitHub
Who uses baikal?
Pros of baikal
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- Build non-linear pipelines effortlessly
- Handle multiple inputs and outputs
- Add steps that operate on targets as part of the pipeline
- Nest pipelines
- Use prediction probabilities (or any other kind of output) as inputs to other steps in the pipeline
- Query intermediate outputs, easing debugging
- Freeze steps that do not require fitting
- Define and add custom steps easily
- Plot pipelines
baikal Alternatives & Comparisons
What are some alternatives to baikal?
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TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.