MeTA
mxnet
| MeTA | mxnet | |
|---|---|---|
| - | 4 | |
| 684 | 20,644 | |
| -0.1% | - | |
| 0.0 | 4.1 | |
| about 1 year ago | 6 months ago | |
| C++ | C++ | |
| MIT License | Apache License 2.0 |
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MeTA
We haven't tracked posts mentioning MeTA yet.
Tracking mentions began in Dec 2020.
mxnet
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List of AI-Models
Click to Learn more...
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Introduction to deep learning hardware in the cloud
Build – Choose a machine learning framework (such as TensorFlow, PyTorch, Apache MXNet, etc.)
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just released my Clojure AI book
Clojure and Python also have bindings to the Apache MXNet library. Is there a reason why you didn't use them in some of your projects?
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Can Apple's M1 help you train models faster and cheaper than Nvidia's V100?
> But you still lose something, e.g. if you use half precision on V100 you get virtually double speed, if you do on a 1080 / 2080 you get... nothing because it's not supported.
That's not true. FP16 is supported and can be fast on 2080, although some frameworks fail to see the speed-up. I filed a bug report about this a year ago: https://github.com/apache/incubator-mxnet/issues/17665
What consumer GPUs lack is ECC and fast FP64.
What are some alternatives?
CCV - C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library
Caffe - Caffe: a fast open framework for deep learning.
Recommender - A C library for product recommendations/suggestions using collaborative filtering (CF)
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Caffe2
vowpal_wabbit - Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
mlpack - mlpack: a fast, header-only C++ machine learning library
Dlib - A toolkit for making real world machine learning and data analysis applications in C++
Porcupine - On-device wake word detection powered by deep learning
Theano - Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as PyTensor: www.github.com/pymc-devs/pytensor