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Keras vs NSFWJS: What are the differences?
What is 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/.
What is NSFWJS? NSFW detection on the client-side via Tensorflow.js. A simple JavaScript library to help you quickly identify unseemly images; all in the client's browser. Currently, it has ~90% accuracy from a test set of 15,000 test images.
Keras and NSFWJS belong to "Machine Learning Tools" category of the tech stack.
Keras and NSFWJS are both open source tools. It seems that Keras with 42.5K GitHub stars and 16.2K forks on GitHub has more adoption than NSFWJS with 3.18K GitHub stars and 172 GitHub forks.
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Learn MorePros of Keras
Pros of NSFWJS
Pros of Keras
- Quality Documentation8
- Supports Tensorflow and Theano backends7
- Easy and fast NN prototyping7
Pros of NSFWJS
- Very Accurate1
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Cons of Keras
Cons of NSFWJS
Cons of Keras
- Hard to debug4
Cons of NSFWJS
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- No public GitHub repository available -
What is Keras?
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
What is NSFWJS?
A simple JavaScript library to help you quickly identify unseemly images; all in the client's browser. Currently, it has ~90% accuracy from a test set of 15,000 test images.
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What companies use NSFWJS?
What companies use Keras?
What companies use NSFWJS?
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What tools integrate with Keras?
What tools integrate with NSFWJS?
What tools integrate with Keras?
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What are some alternatives to Keras and NSFWJS?
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.
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.
MXNet
A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
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.