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age-estimation

Age Estimation Using TensorFlow

clmradiomics

Script to compute the features used in the paper "Prediction of histopathological growth patterns by radiomics and CT-imaging in patients with operable colorectal liver metastases: a proof-of-concept study." M. P. A. Starmans, F. E. Buisman et al. 2019.

cnn_tensorflow

128-features regression from images using a convolutional network in TensorFlow

feature-selection-for-machine-learning

Methods with examples for Feature Selection during Pre-processing in Machine Learning.

house-price-prediction-advanced-regression-techniques

80% accuracy is achieved using feature selection and svm

house-price-prediction-using-multi-images

multiImageInputRegression

houses-dataset

This is the first benchmark dataset for houses prices that contains both images and textual information that was introduced in our paper.

image-regression---age-prediction---fastai

jiqixuexi

open-kbp

Repository to help contestants get started in the OpenKBP: AAPM2020 Grand Challenge dataset, which is also provided.

predictive-maintenance

Data Wrangling, EDA, Feature Engineering, Model Selection, Regression, Binary and Multi-class Classification (Python, scikit-learn)

pyradiomics

Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks.

rtdoseprediction

Automatic Radiotherapy Treatment Planning ， Knowledge-Based Planning ， Dose Prediction ， Cascade 3D Network (C3D) ，DCNN, Head and Neck , 1st Place Solution to the AAPM OpenKBP challenge

tensorflow-learning

B站上炼数成金的公开课笔记

tensorflow-mnist

tensorflow-mnist-cnn

MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.

typhoon_prediction

# typhoon Analysis satellite images of typhoons by deep-learning (CNN), based on PyTorch. This CNN learns to map the satellite images of typhoons to their max wind speed from. The labeled train set is obtained from agora/JMA. ## Requirements * BeautifulSoup * PIL * Pytorch ## Usage 1. Run `download.py`, download the satellite images of typhoons to folder `tys_raw`. 2. Run `create_samples.py`, convert raw data into the legal samples for our CNN, create two new forlder `train_set` and `test_set`. 3. Train CNN using `train_net.py`, the trained CNN will be saved as a disk file `net_relu.pt`. 4. Run `test_net.py`, analysis the test set. After 10 epoches training the CNN regressor reached mean loss about 8 (knots) in train set and about 10 (knots) in test set.  Here is what this CNN thinks of the top 20 typhoons sorted by max wind. ``` 1 ('197920', 130.27679443359375) 2 ('200914', 127.7662582397461) 3 ('199019', 122.92172241210938) 4 ('200918', 122.84004211425781) 5 ('201614', 122.66597747802734) 6 ('201601', 122.03250885009766) 7 ('201513', 121.75947570800781) 8 ('200922', 121.35771942138672) 9 ('201013', 120.0194091796875) 10 ('201330', 118.92587280273438) 11 ('201419', 117.6025390625) 12 ('198305', 117.10270690917969) 13 ('201422', 116.77259063720703) 14 ('198522', 116.46116638183594) 15 ('201327', 116.42304992675781) 16 ('201216', 116.36921691894531) 17 ('198221', 116.18096923828125) 18 ('199230', 115.96656799316406) 19 ('198210', 115.96611022949219) 20 ('201328', 115.57132720947266) ``` ## Tips * Memory should be at least 1.5G . * This project is written without `cuda()`, while you can use `cuda()` to transfer the CNN onto GPU and speedup the training. * The images and labels are crawled from agora.ax.nii.ac.jp/digital-typhoon , and the labels are refered to JMA(Japan Meteorological agency).