Keras vs baikal

Overview

Keras

Stacks1.1K

Followers1.1K

Votes22

baikal

Stacks4

Followers11

Votes0

GitHub Stars590

Forks30

Keras vs baikal: What are the differences?

Keras: Deep Learning library for Theano and TensorFlow. Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/; baikal: A graph-based functional API for building complex scikit-learn pipelines. 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.

Keras and baikal belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Keras are:

neural networks API
Allows for easy and fast prototyping
Convolutional networks support

On the other hand, baikal provides the following key features:

Build non-linear pipelines effortlessly
Handle multiple inputs and outputs
Add steps that operate on targets as part of the pipeline

Keras and baikal are both open source tools. It seems that Keras with 47.4K GitHub stars and 17.9K forks on GitHub has more adoption than baikal with 553 GitHub stars and 23 GitHub forks.

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Advice on Keras, baikal

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

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Detailed Comparison

Keras	baikal
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/	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.
neural networks API;Allows for easy and fast prototyping;Convolutional networks support;Recurent networks support;Runs on GPU	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
Statistics
GitHub Stars -	GitHub Stars 590
GitHub Forks -	GitHub Forks 30
Stacks 1.1K	Stacks 4
Followers 1.1K	Followers 11
Votes 22	Votes 0
Pros & Cons
Pros 8 Quality Documentation 7 Supports Tensorflow and Theano backends 7 Easy and fast NN prototyping Cons 4 Hard to debug	No community feedback yet
Integrations
TensorFlow scikit-learn Python	Python scikit-learn

What are some alternatives to Keras, baikal?

TensorFlow

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.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

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.

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Related Comparisons

Some of the features offered by Keras are:

neural networks API
Allows for easy and fast prototyping
Convolutional networks support

On the other hand, baikal provides the following key features:

Build non-linear pipelines effortlessly
Handle multiple inputs and outputs
Add steps that operate on targets as part of the pipeline

Keras and baikal are both open source tools. It seems that Keras with 47.4K GitHub stars and 17.9K forks on GitHub has more adoption than baikal with 553 GitHub stars and 23 GitHub forks.

Keras vs baikal