CCV
mxnet
Our great sponsors
| CCV | mxnet | |
|---|---|---|
| 3 | 4 | |
| 7,040 | 20,644 | |
| - | - | |
| 9.5 | 4.1 | |
| 20 days ago | 6 months ago | |
| C | C++ | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
CCV
- Modern Image Processing Algorithms Implementation in C
-
[BBC solent sport] Gary Cahill on the verge of joining AFC Bournemouth on a free transfer
CCV?
-
How to extract dominant color of an image?
liuliu/ccv: C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library (github.com)
mxnet
-
List of AI-Models
Click to Learn more...
-
Introduction to deep learning hardware in the cloud
Build – Choose a machine learning framework (such as TensorFlow, PyTorch, Apache MXNet, etc.)
-
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?
-
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?
catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Caffe - Caffe: a fast open framework for deep learning.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
MeTA - A Modern C++ Data Sciences Toolkit
Caffe2
NN++ - A small and easy to use neural net implementation for C++. Just download and #include!
mlpack - mlpack: a fast, header-only C++ machine learning library
Fido - A lightweight C++ machine learning library for embedded electronics and robotics.
Porcupine - On-device wake word detection powered by deep learning
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
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