PyBrain vs TensorFlow

Overview

TensorFlow

Stacks3.9K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

PyBrain

Stacks0

Followers6

Votes0

PyBrain vs TensorFlow: What are the differences?

Development and Maintenance: In terms of development and maintenance, PyBrain is considered to be easier and simpler due to its lightweight design and smaller number of dependencies. On the other hand, TensorFlow requires more effort for development and maintenance due to its more complex architecture and larger codebase.
Flexibility and Customizability: TensorFlow offers greater flexibility and customizability compared to PyBrain. TensorFlow allows users to define their own computational graphs and is more suitable for complex deep learning models, while PyBrain provides limited flexibility in terms of customization.
Community Support: TensorFlow has a much larger and active community compared to PyBrain, which results in more resources, documentation, and support available for users. This extensive community support can be beneficial for troubleshooting issues, sharing knowledge, and staying updated with the latest advancements in deep learning.
Performance and Scalability: TensorFlow is known for its superior performance and scalability, especially when dealing with large datasets and complex models. PyBrain may struggle to match the performance and scalability of TensorFlow when it comes to training deep neural networks on massive amounts of data.
Deployment and Production Readiness: TensorFlow is considered to be more suitable for deployment and production environments, with support for distributed computing, serving models via TensorFlow Serving, and integration with other frameworks and platforms. PyBrain, on the other hand, may lack some deployment capabilities and tools needed for enterprise-level applications.
Ease of Use and Learning Curve: PyBrain is often recommended for beginners in the field of deep learning due to its simplicity and ease of use. On the contrary, TensorFlow has a steeper learning curve and may require more time and effort to get accustomed to its syntax, concepts, and functionality.

In Summary, PyBrain and TensorFlow differ in terms of development and maintenance, flexibility, community support, performance, deployment readiness, and ease of use.

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CLI (Node.js)

Manual

Detailed Comparison

TensorFlow	PyBrain
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.	It's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.
-	Supervised Learning; Unsupervised Learning; Reinforcement Learning; Black-box Optimization; Network Architectures; Toy Environments; 3D Environments; Function Environments; Pole-Balancing
Statistics
GitHub Stars 192.3K	GitHub Stars -
GitHub Forks 74.9K	GitHub Forks -
Stacks 3.9K	Stacks 0
Followers 3.5K	Followers 6
Votes 106	Votes 0
Pros & Cons
Pros 32 High Performance 19 Connect Research and Production 16 Deep Flexibility 12 Auto-Differentiation 11 True Portability Cons 9 Hard 6 Hard to debug 2 Documentation not very helpful	No community feedback yet
Integrations
JavaScript	Python

What are some alternatives to TensorFlow, PyBrain?

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.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

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

PyBrain vs TensorFlow: What are the differences?

Development and Maintenance: In terms of development and maintenance, PyBrain is considered to be easier and simpler due to its lightweight design and smaller number of dependencies. On the other hand, TensorFlow requires more effort for development and maintenance due to its more complex architecture and larger codebase.
Flexibility and Customizability: TensorFlow offers greater flexibility and customizability compared to PyBrain. TensorFlow allows users to define their own computational graphs and is more suitable for complex deep learning models, while PyBrain provides limited flexibility in terms of customization.
Community Support: TensorFlow has a much larger and active community compared to PyBrain, which results in more resources, documentation, and support available for users. This extensive community support can be beneficial for troubleshooting issues, sharing knowledge, and staying updated with the latest advancements in deep learning.
Performance and Scalability: TensorFlow is known for its superior performance and scalability, especially when dealing with large datasets and complex models. PyBrain may struggle to match the performance and scalability of TensorFlow when it comes to training deep neural networks on massive amounts of data.
Deployment and Production Readiness: TensorFlow is considered to be more suitable for deployment and production environments, with support for distributed computing, serving models via TensorFlow Serving, and integration with other frameworks and platforms. PyBrain, on the other hand, may lack some deployment capabilities and tools needed for enterprise-level applications.
Ease of Use and Learning Curve: PyBrain is often recommended for beginners in the field of deep learning due to its simplicity and ease of use. On the contrary, TensorFlow has a steeper learning curve and may require more time and effort to get accustomed to its syntax, concepts, and functionality.

In Summary, PyBrain and TensorFlow differ in terms of development and maintenance, flexibility, community support, performance, deployment readiness, and ease of use.

PyBrain vs TensorFlow

Overview