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AWS DeepLens

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PyBrain vs AWS DeepLens: What are the differences?

Developers describe PyBrain as "A modular Machine Learning Library for Python". 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. On the other hand, AWS DeepLens is detailed as "Deep learning enabled video camera for developers". It helps put machine learning in the hands of developers, literally, with a fully programmable video camera, tutorials, code, and pre-trained models designed to expand deep learning skills.

PyBrain and AWS DeepLens can be categorized as "Machine Learning" tools.

Some of the features offered by PyBrain are:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

On the other hand, AWS DeepLens provides the following key features:

  • A new way to learn machine learning
  • Custom built for deep learning
  • Build custom models with Amazon SageMaker
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What is AWS DeepLens?

It helps put machine learning in the hands of developers, literally, with a fully programmable video camera, tutorials, code, and pre-trained models designed to expand deep learning skills.

What is PyBrain?

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.

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What tools integrate with AWS DeepLens?
What tools integrate with PyBrain?

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What are some alternatives to AWS DeepLens and PyBrain?
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.
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.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
CUDA
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.
See all alternatives