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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Motivation
Currently lots of C++ based unit tests are executed directly from test.sh/win-test.sh for example:
https://github.com/pytorch/pytorch/blob/0bd8d0951dcb4063c0f7552a7404bd7f0e7b6e6f/.jenkins/pytorch/test.sh#L317
Which have following drawbacks:
- It excluded those test runtime from auto-sharding/auto-categorization
- Make them subject of running on only particular platform (
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A curated list of awesome Deep Learning tutorials, projects and communities.
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YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
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Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
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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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Visualizer for neural network, deep learning, and machine learning models
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fitDataset() expects a Dataset that produces elements of a certain shape, with matching batch sizes etc., and throws errors (from standardizeDataIteratorOutput()) when the conditions are not met. These errors should be tested.
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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Roadmap to becoming an Artificial Intelligence Expert in 2021
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ncnn is a high-performance neural network inference framework optimized for the mobile platform
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In gensim/models/fasttext.py:
model = FastText(
vector_size=m.dim,
vector_size=m.dim,
window=m.ws,
window=m.ws,
epochs=m.epoch,
epochs=m.epoch,
negative=m.neg,
negative=m.neg,
# FIXME: these next 2 lines read in unsupported FB FT modes (loss=3 softmax or loss=4 onevsall,
# or model=3 superviA comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
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machine learning and deep learning tutorials, articles and other resources
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The "Python Machine Learning (1st edition)" book code repository and info resource
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New Operator
Describe the operator
Why is this operator necessary? What does it accomplish?
This is a frequently used operator in tensorflow/keras
Can this operator be constructed using existing onnx operators?
If so, why not add it as a function?
I don't know.
Is this operator used by any model currently? Which one?
Are you willing to contribute it?
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
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Deep learning library featuring a higher-level API for TensorFlow.
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MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
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机器学习相关教程
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TensorFlow Tutorials with YouTube Videos
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TensorFlow tutorials and best practices.
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WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.
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Deep Learning and Reinforcement Learning Library for Scientists and Engineers
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A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
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Build your neural network easy and fast, 莫烦Python中文教学
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Pre-trained and Reproduced Deep Learning Models (『飞桨』官方模型库,包含多种学术前沿和工业场景验证的深度学习模型)
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Current implementation of Go binding can not specify options.
GPUOptions struct is in internal package. And
go generatedoesn't work for protobuf directory. So we can't specify GPUOptions forNewSession.