classifier github

github - bhrigu123/classifier: organize files in your directory instantly, by classifying them into different folders

github - bhrigu123/classifier: organize files in your directory instantly, by classifying them into different folders

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

github - mandeer/classifier: classifier by pytorch

github - mandeer/classifier: classifier by pytorch

StochasticDepth ResNeXtBut we argue that it is imprecise to view our method as ensembling, because the members to be aggregated are trained jointly, not independently.

github - yaoyuanzhou/a-classifier-with-pytorch

github - yaoyuanzhou/a-classifier-with-pytorch

This code calls the models in Torchvision, and the classification network topic framework is derived from Torchvision. (And if you have any problem,you can send email to me:[email protected] or leave an error message in Issues.

The above is the classic network framework available within the models, and only for the classification networks within.This code is can take transfer learning , download the ImageNet pre trained initial model and then transfer learning in your code, and can be frozen convolution training only full connection layer, or global training, we only use the convolution of the classic network layer, and then the convolution results set on our lightweight classifier

We used this classifier to predict the gender of the chicken, and we used vgg16,vgg16_bn,vgg19,vgg19_bn,resnet18,resnet34densenet101 made a comparisonYou can get our dataset here()

github - vivamoto/classifier: machine learning code, derivatives calculation and optimization algorithms developed during the machine learning course at universidade de sao paulo. all codes in python, numpy and matplotlib with example in the end of file

github - vivamoto/classifier: machine learning code, derivatives calculation and optimization algorithms developed during the machine learning course at universidade de sao paulo. all codes in python, numpy and matplotlib with example in the end of file

Machine learning code, derivatives calculation and optimization algorithms developed during the Machine Learning course at Universidade de Sao Paulo. All codes in Python, NumPy and Matplotlib with example in the end of file.

Machine learning code, derivatives calculation and optimization algorithms developed during the Machine Learning course at Universidade de Sao Paulo. All codes in Python, NumPy and Matplotlib with example in the end of file.

github - cardmagic/classifier: a general classifier module to allow bayesian and other types of classifications

github - cardmagic/classifier: a general classifier module to allow bayesian and other types of classifications

Notice that LSI will work without these libraries, but as soon as they are installed, Classifier will make use of them. No configuration changes are needed, we like to keep things ridiculously easy for you.

A Latent Semantic Indexer by David Fayram. Latent Semantic Indexing engines are not as fast or as small as Bayesian classifiers, but are more flexible, providing fast search and clustering detection as well as semantic analysis of the text that theoretically simulates human learning.

github - marcotcr/lime: lime: explaining the predictions of any machine learning classifier

github - marcotcr/lime: lime: explaining the predictions of any machine learning classifier

This project is about explaining what machine learning classifiers (or models) are doing. At the moment, we support explaining individual predictions for text classifiers or classifiers that act on tables (numpy arrays of numerical or categorical data) or images, with a package called lime (short for local interpretable model-agnostic explanations). Lime is based on the work presented in this paper (bibtex here for citation). Here is a link to the promo video:

Lime is able to explain any black box classifier, with two or more classes. All we require is that the classifier implements a function that takes in raw text or a numpy array and outputs a probability for each class. Support for scikit-learn classifiers is built-in.

Below are some screenshots of lime explanations. These are generated in html, and can be easily produced and embedded in ipython notebooks. We also support visualizations using matplotlib, although they don't look as nice as these ones.

Negative (blue) words indicate atheism, while positive (orange) words indicate christian. The way to interpret the weights by applying them to the prediction probabilities. For example, if we remove the words Host and NNTP from the document, we expect the classifier to predict atheism with probability 0.58 - 0.14 - 0.11 = 0.31.

Intuitively, an explanation is a local linear approximation of the model's behaviour. While the model may be very complex globally, it is easier to approximate it around the vicinity of a particular instance. While treating the model as a black box, we perturb the instance we want to explain and learn a sparse linear model around it, as an explanation. The figure below illustrates the intuition for this procedure. The model's decision function is represented by the blue/pink background, and is clearly nonlinear. The bright red cross is the instance being explained (let's call it X). We sample instances around X, and weight them according to their proximity to X (weight here is indicated by size). We then learn a linear model (dashed line) that approximates the model well in the vicinity of X, but not necessarily globally. For more information, read our paper, or take a look at this blog post.

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