Python For Data ScientistsDeliverable: This Jupyter notebook will help aspiring data scientists learn and practice the necessary python code needed for many data science projects.
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Kaggle CompetitionsThere are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.
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Doc BrowserA documentation browser with support for DevDocs, Dash and Hoogle, written in Haskell and QML
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WotanAutomagically remove trends from time-series data
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Pythonplot.com📈 Interactive comparison of Python plotting libraries for exploratory data analysis. Examples of using Pandas plotting, plotnine, Seaborn, and Matplotlib.
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Prob mbrlA library of probabilistic model based RL algorithms in pytorch
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IntrodatasciCourse materials for: Introduction to Data Science and Programming
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DvrlDeep Variational Reinforcement Learning
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Training MaterialA collection of code examples as well as presentations for training purposes
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Ox Ipynborg-mode exporter to Jupyter notebooks
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ZeroshotknowledgetransferAccompanying code for the paper "Zero-shot Knowledge Transfer via Adversarial Belief Matching"
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Knet.jlKoç University deep learning framework.
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HmmAn implementation of the Viterbi Algorithm for training Hidden Markov models. This repo accompanies the video found here: https://www.youtube.com/watch?v=kqSzLo9fenk
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GemfieldGemfield homework or libgemfield.so
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Ngsim envLearning human driver models from NGSIM data with imitation learning.
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Glove As A Tensorflow Embedding LayerTaking a pretrained GloVe model, and using it as a TensorFlow embedding weight layer **inside the GPU**. Therefore, you only need to send the index of the words through the GPU data transfer bus, reducing data transfer overhead.
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Ds With PysimpleguiData science and Machine Learning GUI programs/ desktop apps with PySimpleGUI package
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MobilenetssdfaceCaffe implementation of Mobilenet-SSD face detector (NCS compatible)
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CognomaPutting machine learning in the hands of cancer biologists
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Rna Seq TsneThe art of using t-SNE for single-cell transcriptomics
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Spark Py NotebooksApache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks
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Resnet cnn mri adniCode for Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification paper
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JupytemplateTemplates for jupyter notebooks
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Turkish Bert Nlp PipelineBert-base NLP pipeline for Turkish, Ner, Sentiment Analysis, Question Answering etc.
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Sprint ganPrivacy-preserving generative deep neural networks support clinical data sharing
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2014 Summer TravelsPython-based spatial data analysis and visualization of the GPS location data from my 2014 summer travels.
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MachinelearningA repo with tutorials for algorithms from scratch
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Hops ExamplesExamples for Deep Learning/Feature Store/Spark/Flink/Hive/Kafka jobs and Jupyter notebooks on Hops
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Delf enhancedWrapper of DELF Tensorflow Model
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PancancerBuilding classifiers using cancer transcriptomes across 33 different cancer-types
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Lis Ynp🔮 Life is short, you need PyTorch.
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Chainer HandsonCAUTION: This is not maintained anymore. Visit https://github.com/chainer-community/chainer-colab-notebook/
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Cheat SheetsA cheat sheet can be really helpful when you're trying a set of exercises related to a specific topic, or working on a project. Because you can only fit so much information on a single sheet of paper, most cheat sheets are a simple listing of syntax rules. This set of cheat sheets aims to remind you of syntax rules, but also remind you of important concepts as well.
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Pulmonary Nodules SegmentationTianchi medical AI competition [Season 1]: Lung nodules image segmentation of U-Net. U-Net训练基于卷积神经网络的肺结节分割器
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Book Code《深度学习之PyTorch实战计算机视觉》全书代码
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JunosautomationTo contain example scripts for different tools.
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DeeplearningPython implementation of Deep Learning book
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Deep Residual UnetResUNet, a semantic segmentation model inspired by the deep residual learning and UNet. An architecture that take advantages from both(Residual and UNet) models.
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XpediteA non-sampling profiler purpose built to measure and optimize performance of ultra low latency/real time systems
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