Neural Network
Artificial neural networks (ANN) are computational systems that "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.
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🐛 Bug
As discussed here, calling kthvalue like torch.kthvalue(a, k, out=(a, indices)) produces a buffer overflow. Natalia suggested disallowing outputs that overlap with the input.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
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Nov 29, 2020 - Jupyter Notebook
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Dec 15, 2020 - Python
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
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Sep 8, 2020 - C++
A curated list of awesome Deep Learning tutorials, projects and communities.
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Dec 8, 2020
Reference from TensorFlow: https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/matrix-band-part
This op is used by the Music Transformer model.
YOLOv4v / Scaled-YOLOv4 - Neural Networks for Object Detection (Windows and Linux version of Darknet )
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Dec 15, 2020 - C
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
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Dec 15, 2020 - Python
pytorch handbook是一本开源的书籍,目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门,其中包含的Pytorch教程全部通过测试保证可以成功运行
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Nov 6, 2020 - Jupyter Notebook
Visualizer for neural network, deep learning, and machine learning models
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Dec 15, 2020 - JavaScript
We plan to gradually migrate brain.js to TypeScript, code base is pretty large, so we would love your help!
How to contribute?
- Convert a file from .js to .ts
- Add types, fix all type errors.
- Submit a PR!
🎉
Here you can find a guide on how to contribute.
Want to convert something, let us know in the comment an
Not a high-priority at all, but it'd be more sensible for such a tutorial/testing utility corpus to be implemented elsewhere - maybe under /test/ or some other data- or doc- related module – rather than in gensim.models.word2vec.
Originally posted by @gojomo in RaRe-Technologies/gensim#2939 (comment)
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
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Dec 7, 2020
The "Python Machine Learning (1st edition)" book code repository and info resource
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Oct 16, 2020 - Jupyter Notebook
machine learning and deep learning tutorials, articles and other resources
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Nov 30, 2020
ncnn is a high-performance neural network inference framework optimized for the mobile platform
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Dec 15, 2020 - C++
Decensoring Hentai with Deep Neural Networks
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Nov 13, 2020 - Python
Bug Report
These tests were run on s390x. s390x is big-endian architecture.
Failure log for helper_test.py
________________________________________________ TestHelperTensorFunctions.test_make_tensor ________________________________________________
self = <helper_test.TestHelperTensorFunctions testMethod=test_make_tensor>
def test_make_tensor(self): # type: () -> None
Deep learning library featuring a higher-level API for TensorFlow.
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Nov 30, 2020 - Python
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
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Sep 27, 2019 - Java
机器学习相关教程
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Oct 31, 2020 - Python
TensorFlow tutorials and best practices.
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Oct 22, 2020
TensorFlow Tutorials with YouTube Videos
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Aug 10, 2020 - Jupyter Notebook
WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.
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Aug 14, 2019 - TypeScript
What would you like to be added: As title
Why is this needed: All pruning schedule except AGPPruner only support level, L1, L2. While there are FPGM, APoZ, MeanActivation and Taylor, it would be much better if we can choose any pruner with any pruning schedule.
**Without this feature, how does current nni
Roadmap to becoming an Artificial Intelligence Expert in 2020
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Dec 11, 2020 - JavaScript
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
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Dec 15, 2020 - Python
A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
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Dec 1, 2020
Hi I would like to propose a better implementation for 'test_indices':
We can remove the unneeded np.array casting:
Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))
Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))


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