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numericaal vs TensorFlow.js: What are the differences?
Developers describe numericaal as "Machine learning for mobile & IoT made easy". numericaal automates model optimization and management so you can focus on data and training. On the other hand, TensorFlow.js is detailed as "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.
numericaal and TensorFlow.js can be categorized as "Machine Learning" tools.
TensorFlow.js is an open source tool with 11.2K GitHub stars and 816 GitHub forks. Here's a link to TensorFlow.js's open source repository on GitHub.
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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 numericaal?
numericaal automates model optimization and management so you can focus on data and training.
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 numericaal?
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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.