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TensorFlow.js vs Caffe: What are the differences?
TensorFlow.js: Machine Learning in JavaScript. 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; Caffe: A deep learning framework. It is a deep learning framework made with expression, speed, and modularity in mind.
TensorFlow.js and Caffe belong to "Machine Learning Tools" category of the tech stack.
TensorFlow.js and Caffe are both open source tools. It seems that Caffe with 29.2K GitHub stars and 17.6K forks on GitHub has more adoption than TensorFlow.js with 11.8K GitHub stars and 913 GitHub forks.
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Pros of TensorFlow.js
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Pros of TensorFlow.js
- Open Source6
- NodeJS Powered5
- Deploy python ML model directly into javascript2
- Cost - no server needed for inference1
- Privacy - no data sent to server1
- Runs Client Side on device1
- Can run TFJS on backend, frontend, react native, + IOT1
- Easy to share and use - get more eyes on your research1
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What is Caffe?
It is a deep learning framework made with expression, speed, and modularity in mind.
What is 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
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What tools integrate with Caffe?
What tools integrate with TensorFlow.js?
What tools integrate with Caffe?
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What are some alternatives to Caffe and TensorFlow.js?
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.
Torch
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
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.
Caffe2
Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/