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-multibeam

Python module for handling multibeam sonar data

-pointcloudsvmdemo

三维点云激光分类（建筑，树木）

attention-module

Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"

autoecg

Model and transfer learning model based on Automatic diagnosis of the 12-lead ECG using a deep neural network, Ribeiro, et al.

awesome-deep-learning-of-remote-sensing

Awesome Deep Learning of Remote Sensing

awesome-transfer-learning

Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)

benthicanalysistool

Matlab GUI for semi-automation and annotation of deep-sea ROV benthic imagery

blog_md

cnn-svm

An Architecture Combining Convolutional Neural Network (CNN) and Linear Support Vector Machine (SVM) for Image Classification

cnn_fe

deepcraters

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

dlaicourse

Notebooks for learning deep learning

e2ec

E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation

eat_tensorflow2_in_30_days

Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋

finetune_alexnet_with_tensorflow

Code for finetuning AlexNet in TensorFlow >= 1.2rc0

grad_cam_pytorch-1.01

CNN可视化代码，帮助了解建立GradCam过程， 代码中文注解

hands-on-transfer-learning-with-python

Deep learning simplified by transferring prior learning using the Python deep learning ecosystem

image-contrast-enhancement

Python implementation of "A New Image Contrast Enhancement Algorithm Using Exposure Fusion Framework", CAIP2017

iptv

Collection of 5000+ publicly available IPTV channels from all over the world

landslide-ea

Exploratory analysis based on landslide dataset from NASA

lstm-neural-network-for-time-series-prediction

LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

machine-learning-models

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

malaria-detection-using-cnn-and-transfer-learning

The data-set contains cells of 2 classes that are Infected and Uninfected. Here pre-trained model vgg-19 is used for transfer learning and the it is being trained for only 5 epochs.

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

ml-dl

ml-interpretability-european-football

Understanding gender differences in professional European football through Machine Learning interpretability and match actions data. This repository contains the full data pipeline implemented for the study *Understanding gender differences in professional European football through Machine Learning interpretability and match actions data*. We evaluated European male, and female football players' main differential features in-match actions data under the assumption of finding significant differences and established patterns between genders. A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. Female (1511) and male (2700) data points were collected from event data categorized by game period and player position. Each data point included the main tactical variables supported by research and industry to evaluate and classify football styles and performance. We set up a supervised classification pipeline to predict the gender of each player by looking at their actions in the game. The comparison methodology did not include any qualitative enrichment or subjective analysis to prevent biased data enhancement or gender-related processing. The pipeline had three representative binary classification models; A logic-based Decision Trees, a probabilistic Logistic Regression and a multilevel perceptron Neural Network. Each model tried to draw the differences between male and female data points, and we extracted the results using machine learning explainability methods to understand the underlying mechanics of the models implemented. A good model predicting accuracy was consistent across the different models deployed. ## Installation Install the required python packages ``` pip install -r requirements.txt ``` To handle heterogeneity and performance efficiently, we use PySpark from [Apache Spark](https://spark.apache.org/). PySpark enables an end-user API for Spark jobs. You might want to check how to set up a local or remote Spark cluster in [their documentation](https://spark.apache.org/docs/latest/api/python/index.html). ## Repository structure This repository is organized as follows: - Preprocessed data from the two different data streams is collecting in [the data folder](data/). For the Opta files, it contains the event-based metrics computed from each match of the 2017 Women's Championship and a single file calculating the event-based metrics from the 2016 Men's Championship published [here](https://figshare.com/collections/Soccer_match_event_dataset/4415000/5). Even though we cannot publish the original data source, the two python scripts implemented to homogenize and integrate both data streams into event-based metrics are included in [the data gathering folder](data_gathering/) folder contains the graphical images and media used for the report. - The [data cleaning folder](data_cleaning/) contains descriptor scripts for both data streams and [the final integration](data_cleaning/merger.py) - [Classification](classification/) contains all the Jupyter notebooks for each model present in the experiment as well as some persistent models for testing.

mn-landslide-susceptibility-

University of Minnesota student project from GEOG 5541 Principles of Geocomputing to measure landslide susceptibility using LiDAR derived datasets.

numpy-ml

Machine learning, in numpy

object-detection-on-satellite-images-using-mask-r-cnn