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  </channel><item rdf:about="https://www.bostongis.com/blog/index.php?/archives/272-pgAdmin4-now-offers-PostGIS-geometry-viewer.html#extended">
    <title>pgAdmin4 now offers PostGIS geometry viewer - BostonGIS</title>
    <dc:date>2019-08-23T15:16:54+00:00</dc:date>
    <link>https://www.bostongis.com/blog/index.php?/archives/272-pgAdmin4-now-offers-PostGIS-geometry-viewer.html#extended</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[pgAdmin4 version 3.3 released this week comes with a PostGIS geometry viewer. You will be able to see the graphical output of your query directly in pgAdmin, provided you output a geometry or geography column. If your column is of SRID 4326 (WGS 84 lon/lat), pgAdmin will automatically display against an OpenStreetMap background.]]></description>
<dc:subject>postfix postgis gis databases</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://developers.google.com/machine-learning/crash-course/">
    <title>Machine Learning Crash Course  |  Google Developers</title>
    <dc:date>2019-03-20T16:51:47+00:00</dc:date>
    <link>https://developers.google.com/machine-learning/crash-course/</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[Learn and apply fundamental machine learning concepts with the Crash Course, get real-world experience with the companion Kaggle competition, or visit Learn with Google AI to explore the full library of training resources.]]></description>
<dc:subject>deeplearning machinelearning keras tensorflow education capstone classes google</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://medium.com/@hanrelan/a-non-experts-guide-to-image-segmentation-using-deep-neural-nets-dda5022f6282">
    <title>A Non-Expert’s Guide to Image Segmentation Using Deep Neural Nets</title>
    <dc:date>2019-03-18T14:57:15+00:00</dc:date>
    <link>https://medium.com/@hanrelan/a-non-experts-guide-to-image-segmentation-using-deep-neural-nets-dda5022f6282</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[Given a single picture of a piece of furniture in context, can you automatically separate the furniture from the background?

In this post, I’ll walk through how we can use the current state-of-the-art in deep learning to try and solve this problem. I’m not an expert in machine learning myself, so my hope is that this post will be useful to other non-experts looking to use this powerful new tool.

]]></description>
<dc:subject>temple capstone python keras segmentation</dc:subject>
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    <title>ADE20K dataset</title>
    <dc:date>2019-03-18T14:47:58+00:00</dc:date>
    <link>https://groups.csail.mit.edu/vision/datasets/ADE20K/</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[Images and annotations:

Each folder contains images separated by scene category (same scene categories than the Places Database). For each image, the object and part segmentations are stored in two different png files. All object and part instances are annotated sparately.]]></description>
<dc:subject>temple capstone deeplearning data datasets</dc:subject>
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<dc:identifier>https://pinboard.in/u:cschrader/b:2450bc9ab891/</dc:identifier>
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    <title>udacity/CarND-Semantic-Segmentation</title>
    <dc:date>2019-03-13T21:37:40+00:00</dc:date>
    <link>https://github.com/udacity/CarND-Semantic-Segmentation</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[In this project, you'll label the pixels of a road in images using a Fully Convolutional Network (FCN).]]></description>
<dc:subject>temple capstone remotesensing python keras segmentation udacity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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    <title>FCN - Full Code</title>
    <dc:date>2019-03-13T21:37:35+00:00</dc:date>
    <link>https://gist.github.com/khanhnamle1994/e2ff59ddca93c0205ac4e566d40b5e88</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[code and comments are both helpful. explanation in medium post:
https://medium.com/nanonets/how-to-do-image-segmentation-using-deep-learning-c673cc5862ef]]></description>
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<item rdf:about="http://blog.kaggle.com/2017/05/09/dstl-satellite-imagery-competition-3rd-place-winners-interview-vladimir-sergey/">
    <title>Dstl Satellite Imagery Competition, 3rd Place Winners’ Interview: Vladimir &amp; Sergey | No Free Hunch</title>
    <dc:date>2019-03-13T21:27:56+00:00</dc:date>
    <link>http://blog.kaggle.com/2017/05/09/dstl-satellite-imagery-competition-3rd-place-winners-interview-vladimir-sergey/</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[In their satellite imagery competition, the Defence Science and Technology Laboratory (Dstl) challenged Kagglers to apply novel techniques to "train an eye in the sky". From December 2016 to March 2017, 419 teams competed in this image segmentation challenge to detect and label 10 classes of objects including waterways, vehicles, and buildings. In this winners' interview, Vladimir and Sergey provide detailed insight into their 3rd place solution]]></description>
<dc:subject>temple capstone remotesensing python keras segmentation dstl</dc:subject>
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<item rdf:about="https://deepsense.ai/deep-learning-for-satellite-imagery-via-image-segmentation/">
    <title>Deep learning for satellite imagery via image segmentation | deepsense.ai</title>
    <dc:date>2019-03-13T21:27:22+00:00</dc:date>
    <link>https://deepsense.ai/deep-learning-for-satellite-imagery-via-image-segmentation/</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[In the recent Kaggle competition Dstl Satellite Imagery Feature Detection our deepsense.ai team won 4th place among 419 teams. We applied a modified U-Net – an artificial neural network for image segmentation. In this blog post we wish to present our deep learning solution and share the lessons that we have learnt in the process with you.]]></description>
<dc:subject>temple capstone remotesensing python keras segmentation dstl</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:f89a208557f5/</dc:identifier>
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<item rdf:about="https://github.com/ternaus/kaggle_dstl_submission">
    <title>ternaus/kaggle_dstl_submission: Code for a winning model (3 out of 419) in a Dstl Satellite Imagery Feature Detection challenge</title>
    <dc:date>2019-03-13T21:27:06+00:00</dc:date>
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    <dc:creator>cschrader</dc:creator><dc:subject>temple capstone remotesensing python keras segmentation dstl</dc:subject>
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<item rdf:about="https://medium.com/vooban-ai/satellite-image-segmentation-a-workflow-with-u-net-7ff992b2a56e">
    <title>Satellite Image Segmentation: a Workflow with U-Net</title>
    <dc:date>2019-03-13T21:26:35+00:00</dc:date>
    <link>https://medium.com/vooban-ai/satellite-image-segmentation-a-workflow-with-u-net-7ff992b2a56e</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[In the past few months, I have worked on such an image classifier which goal is to precisely identify objects in satellite images. This was done by training a few U-Net Convolutional Neural Networks (one per category of object — class — to predict) with Keras and TensorFlow, using GPU servers in the cloud.]]></description>
<dc:subject>temple capstone remotesensing python keras segmentation dstl</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:e21164d8fee0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:segmentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:dstl"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://medium.com/nanonets/how-to-do-image-segmentation-using-deep-learning-c673cc5862ef">
    <title>How to do Semantic Segmentation using Deep learning</title>
    <dc:date>2019-03-13T21:26:07+00:00</dc:date>
    <link>https://medium.com/nanonets/how-to-do-image-segmentation-using-deep-learning-c673cc5862ef</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[With the popularity of deep learning in recent years, many semantic segmentation problems are being tackled using deep architectures, most often Convolutional Neural Nets, which surpass other approaches by a large margin in terms of accuracy and efficiency.

]]></description>
<dc:subject>temple capstone remotesensing python keras segmentation udacity khanhnamle1994</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:ab65dfa418c8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:segmentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:udacity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:khanhnamle1994"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://medium.com/@chrieke/essential-geospatial-python-libraries-5d82fcc38731">
    <title>Essential geospatial Python libraries – Christoph Rieke – Medium</title>
    <dc:date>2019-03-13T18:19:21+00:00</dc:date>
    <link>https://medium.com/@chrieke/essential-geospatial-python-libraries-5d82fcc38731</link>
    <dc:creator>cschrader</dc:creator><dc:subject>python library development gis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:5c5a05f5354f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:library"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:development"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:gis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://eo-learn.sentinel-hub.com/">
    <title>eo-learn.sentinel-hub.com</title>
    <dc:date>2019-03-13T15:13:17+00:00</dc:date>
    <link>http://eo-learn.sentinel-hub.com/</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[This example dataset will help you get started with Remote Sensing data and analysis in the open-source framework of eo-learn.

To promote the use of eo-learn, we have decided to share a dataset of EOPatches for the whole region of Slovenia for the year 2017. This data can be used in remote sensing applications, such as land cover classification.

]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras R remotesensing data datasets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:d984a860eeb7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:datasets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/robmarkcole/satellite-image-deep-learning">
    <title>robmarkcole/satellite-image-deep-learning: Resources for performing deep learning on satellite imagery</title>
    <dc:date>2019-03-13T15:12:58+00:00</dc:date>
    <link>https://github.com/robmarkcole/satellite-image-deep-learning</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[This document primarily lists resources for performing deep learning (DL) on satellite imagery. To a lesser extent Machine learning (ML, e.g. random forests, stochastic gradient descent) are also discussed, as are classical image processing techniques]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras R remotesensing data datasets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:ea80d6f5816a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:datasets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.researchgate.net/post/Where_can_I_download_training_data_with_labelled_classes_for_land_cover_land_use_classification3">
    <title>Where can I download training data (with labelled classes) for land cover/land use classification?</title>
    <dc:date>2019-03-13T14:56:53+00:00</dc:date>
    <link>https://www.researchgate.net/post/Where_can_I_download_training_data_with_labelled_classes_for_land_cover_land_use_classification3</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[I am performing land cover land use classification using Sentinel 1 and Sentinel 2 data. Since, in Sentinel 1 (SAR) data, the classes cannot be differentiated visibly, I need a way to develop the training dataset with labelled classes. Is there any website available where training dataset is available? I have already download the Sentinel 1 and Sentinel 2 data from ESA Copernicus hub. Thank you]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras python remotesensing data datasets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:f8b7236202e0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:datasets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://medium.com/@pushkarmandot/build-your-first-deep-learning-neural-network-model-using-keras-in-python-a90b5864116d">
    <title>Build your First Deep Learning Neural Network Model using Keras in Python</title>
    <dc:date>2019-03-13T14:56:00+00:00</dc:date>
    <link>https://medium.com/@pushkarmandot/build-your-first-deep-learning-neural-network-model-using-keras-in-python-a90b5864116d</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[ basic aim is to predict customer churn for a certain bank i.e. which customer is going to leave this bank service. Dataset is small(for learning purpose) and contains 10000 rows with 14 columns. I am not explaining data in detail as dataset is self explanatory. You can download data from my drive]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras python remotesensing data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:74a321a28ead/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.earthdatascience.org/courses/earth-analytics-python/lidar-raster-data/classify-plot-raster-data-in-python/">
    <title>Classify and Plot Raster Data in Python | Earth Data Science - Earth Lab</title>
    <dc:date>2019-03-13T14:55:30+00:00</dc:date>
    <link>https://www.earthdatascience.org/courses/earth-analytics-python/lidar-raster-data/classify-plot-raster-data-in-python/</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[Manually Reclassifying Raster Data
In this lesson, you will learn how to reclassify a raster dataset in Python. When you reclassify a raster, you create a new raster object / file that can be exported and shared with colleagues and / or open in other tools such as QGIS. In that raster each pixel is mapped to a new value based on some approach. This approach can vary depending upon your science question]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras python remotesensing data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:b55613b5e580/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/shakasom/Deep-Learning-for-Satellite-Imagery/blob/master/LULC_Final.ipynb">
    <title>Deep-Learning-for-Satellite-Imagery/LULC_Final.ipynb at master · shakasom/Deep-Learning-for-Satellite-Imagery</title>
    <dc:date>2019-03-13T14:54:32+00:00</dc:date>
    <link>https://github.com/shakasom/Deep-Learning-for-Satellite-Imagery/blob/master/LULC_Final.ipynb</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[This a mini-project to classify 9 Land use classes using transfer learning in Convolutional Neural Networks (CNN). The Dataset used in this project is published with the original paper tittled: EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras python remotesensing data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:79e1a7df7fbc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://towardsdatascience.com/land-use-land-cover-classification-with-deep-learning-9a5041095ddb">
    <title>Land use/Land cover classification with Deep Learning</title>
    <dc:date>2019-03-13T14:53:26+00:00</dc:date>
    <link>https://towardsdatascience.com/land-use-land-cover-classification-with-deep-learning-9a5041095ddb</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[In this project, I used the freely available Sentinel-2 satellite images to classify 9 land use classes and 24000 labeled images ( Figure 2). The original dataset contains 10 classes and 27000 labeled images and is available here]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras python remotesensing data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:c2f0aa9a1f10/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://stackoverflow.com/questions/46440273/error-in-getting-confusion-matrix">
    <title>python - Error in getting confusion matrix - Stack Overflow</title>
    <dc:date>2019-03-12T20:39:13+00:00</dc:date>
    <link>https://stackoverflow.com/questions/46440273/error-in-getting-confusion-matrix</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[confusion_matrix expects the true and predicted class labels, not one-hot/probability distribution representations. Replace the last line with the following:

confusion_matrix(y_test.argmax(axis=1), y_pred.argmax(axis=1))
This will convert the 10000x10 format to 10000 corresponding to the predicted class for each sample.

]]></description>
<dc:subject>python scikit-learn datascience</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:ac7f26413d23/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:scikit-learn"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:datascience"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://stackoverflow.com/questions/47356433/multilabel-binarizer-getting-the-inverse-transform">
    <title>python - Multilabel binarizer - getting the inverse transform - Stack Overflow</title>
    <dc:date>2019-03-12T20:15:08+00:00</dc:date>
    <link>https://stackoverflow.com/questions/47356433/multilabel-binarizer-getting-the-inverse-transform</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[Output of mlb.fit_transform(_labels) will be the input to the inverse_transform.

More on it is here: Multilabel Binarizer

]]></description>
<dc:subject>python temple capstone</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:9f2bf46b1293/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/OpenGeoscience/geonotebook">
    <title>OpenGeoscience/geonotebook: A Jupyter notebook extension for geospatial visualization and analysis</title>
    <dc:date>2019-03-12T19:27:19+00:00</dc:date>
    <link>https://github.com/OpenGeoscience/geonotebook</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[GeoNotebook is an application that provides client/server environment with interactive visualization and analysis capabilities using Jupyter, GeoJS and other open source tools. Jointly developed by Kitware and NASA Ames.

]]></description>
<dc:subject>jupyter gis python tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:5fb6e0c5c045/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:jupyter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:gis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://cs231n.github.io/">
    <title>CS231n Convolutional Neural Networks for Visual Recognition</title>
    <dc:date>2019-03-12T18:40:42+00:00</dc:date>
    <link>http://cs231n.github.io/</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[These notes accompany the Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition. 
For questions/concerns/bug reports contact Justin Johnson regarding the assignments, or contact Andrej Karpathy regarding the course notes. You can also submit a pull request directly to our git repo. 
We encourage the use of the hypothes.is extension to annote comments and discuss these notes inline.]]></description>
<dc:subject>deeplearning machinelearning keras tensorflow education capstone classes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:5a7de5501bb7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:classes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html">
    <title>Building powerful image classification models using very little data</title>
    <dc:date>2019-03-12T02:15:13+00:00</dc:date>
    <link>https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[In this tutorial, we will present a few simple yet effective methods that you can use to build a powerful image classifier, using only very few training examples --just a few hundred or thousand pictures from each class you want to be able to recognize.]]></description>
<dc:subject>python temple capstone datascience deeplearning keras satellite remotesensing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:816dd97b50cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:datascience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:satellite"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/reachsumit/deep-unet-for-satellite-image-segmentation">
    <title>GitHub - reachsumit/deep-unet-for-satellite-image-segmentation: Satellite Imagery Feature Detection with SpaceNet dataset using deep UNet</title>
    <dc:date>2019-03-12T02:14:09+00:00</dc:date>
    <link>https://github.com/reachsumit/deep-unet-for-satellite-image-segmentation</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[This is a Keras based implementation of a deep UNet that performs satellite image segmentation]]></description>
<dc:subject>python temple capstone datascience deeplearning keras satellite remotesensing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:ef5eb8ae5656/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:datascience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:satellite"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/gregbehm/UC-Merced-Pretrained-CNN">
    <title>GitHub - gregbehm/UC-Merced-Pretrained-CNN: Image classification by transfer learning in Keras.</title>
    <dc:date>2019-03-12T02:13:34+00:00</dc:date>
    <link>https://github.com/gregbehm/UC-Merced-Pretrained-CNN</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[This project notebook demonstrates the effectiveness of transfer learning using the Keras deep learning library to classify images from the small UC Merced Land Use dataset. This dataset consists of 2,100 images from 21 classes (100 images per class), derived from the USGS National Map Urban Area Imagery collection]]></description>
<dc:subject>python temple capstone datascience deeplearning keras satellite remotesensing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:21d08b82d738/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:datascience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:satellite"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.kaggle.com/arpandhatt/satellite-image-classification/notebook">
    <title>Satellite Image Classification | Kaggle</title>
    <dc:date>2019-03-12T02:12:38+00:00</dc:date>
    <link>https://www.kaggle.com/arpandhatt/satellite-image-classification/notebook</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[The input data was encoded into CSV files. The X_test_sat4.csv flattened the images that were 28 x 28 x 4 that were taken from space. The first three channels are the standard red, green, and blue channels in normal images. The 4th is a near-infrared band.]]></description>
<dc:subject>python temple capstone datascience deeplearning keras satellite remotesensing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:54b65a0b4da3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:datascience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
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</item>
<item rdf:about="https://stackoverflow.com/questions/34213199/scikit-learn-multilabel-classification-valueerror-you-appear-to-be-using-a-leg">
    <title>python - Scikit Learn Multilabel Classification: ValueError: You appear to be using a legacy multi-label data representation - Stack Overflow</title>
    <dc:date>2019-03-11T19:37:43+00:00</dc:date>
    <link>https://stackoverflow.com/questions/34213199/scikit-learn-multilabel-classification-valueerror-you-appear-to-be-using-a-leg</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[The documents give this example:

>>> from sklearn.preprocessing import MultiLabelBinarizer
>>> y = [[2, 3, 4], [2], [0, 1, 3], [0, 1, 2, 3, 4], [0, 1, 2]]
>>> MultiLabelBinarizer().fit_transform(y)
array([[0, 0, 1, 1, 1],
       [0, 0, 1, 0, 0],
       [1, 1, 0, 1, 0],
       [1, 1, 1, 1, 1],
       [1, 1, 1, 0, 0]])
MultiLabelBinarizer.fit_transform takes in your labeled sets and can output the binary array. The output should then be alright to pass to your fit function]]></description>
<dc:subject>capstone temple python scikit-learn datascience deeplearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:1bb655cb9bfa/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:scikit-learn"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:datascience"/>
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</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://stackoverflow.com/questions/10346336/list-of-lists-into-numpy-array">
    <title>python - List of lists into numpy array - Stack Overflow</title>
    <dc:date>2019-03-11T19:02:27+00:00</dc:date>
    <link>https://stackoverflow.com/questions/10346336/list-of-lists-into-numpy-array</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[If your list of lists contains lists with varying number of elements then the answer of Ignacio Vazquez-Abrams will not work. Instead there are 3 options:

]]></description>
<dc:subject>python temple capstone datascience deeplearning keras</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:c0df18314d8c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:datascience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://datascience.stackexchange.com/questions/9443/when-to-use-one-hot-encoding-vs-labelencoder-vs-dictvectorizor">
    <title>scikit learn - When to use One Hot Encoding vs LabelEncoder vs DictVectorizor? - Data Science Stack Exchange</title>
    <dc:date>2019-03-11T17:27:30+00:00</dc:date>
    <link>https://datascience.stackexchange.com/questions/9443/when-to-use-one-hot-encoding-vs-labelencoder-vs-dictvectorizor</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[I understand the difference between OHE, LabelEncoder and DictVectorizor in terms of what they are doing to the data, but what is not clear to me is when you might choose to employ one technique over another.

Are there certain algorithms or situations in which one has advantages/disadvantages with respect to the others?]]></description>
<dc:subject>keras python temple capstone datascience deeplearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:07529ceadec5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:datascience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://jovianlin.io/keras-one-hot-encode-decode-sequence-data/">
    <title>Keras: One-hot Encode/Decode Sequence Data</title>
    <dc:date>2019-03-11T15:31:14+00:00</dc:date>
    <link>https://jovianlin.io/keras-one-hot-encode-decode-sequence-data/</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[A one-hot encoding is a representation of categorical variables (e.g. cat, dog, rat) as binary vectors (e.g. [1,0,0], [0,1,0], [0,0,1]).]]></description>
<dc:subject>temple keras deeplearning python capstone</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:0e48acdd87e3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://medium.com/@kylepob61392/airplane-image-classification-using-a-keras-cnn-22be506fdb53">
    <title>Airplane Image Classification using a Keras CNN – Kyle O'Brien – Medium</title>
    <dc:date>2019-02-26T20:38:22+00:00</dc:date>
    <link>https://medium.com/@kylepob61392/airplane-image-classification-using-a-keras-cnn-22be506fdb53</link>
    <dc:creator>cschrader</dc:creator><dc:subject>temple capstone deeplearning tensorflow keras python remotesensing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:45a7b7fd4c60/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
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</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://stackoverflow.com/questions/10715965/add-one-row-to-pandas-dataframe">
    <title>python - Add one row to pandas DataFrame - Stack Overflow</title>
    <dc:date>2019-02-26T17:17:03+00:00</dc:date>
    <link>https://stackoverflow.com/questions/10715965/add-one-row-to-pandas-dataframe</link>
    <dc:creator>cschrader</dc:creator><dc:subject>python temple capstone pandas</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:8f835addbd58/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:pandas"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.datacamp.com/community/tutorials/kaggle-tutorial-machine-learning">
    <title>Kaggle Tutorial: Your First ML Model (article) - DataCamp</title>
    <dc:date>2019-02-21T22:19:00+00:00</dc:date>
    <link>https://www.datacamp.com/community/tutorials/kaggle-tutorial-machine-learning</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[label]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras python remotesensing data kaggle</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:101006db5f05/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:kaggle"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.dataquest.io/blog/kaggle-fundamentals/">
    <title>Kaggle Fundamentals: The Titanic Competition</title>
    <dc:date>2019-02-21T22:18:55+00:00</dc:date>
    <link>https://www.dataquest.io/blog/kaggle-fundamentals/</link>
    <dc:creator>cschrader</dc:creator><dc:subject>temple capstone deeplearning tensorflow keras python remotesensing data kaggle</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:047985df7b69/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:kaggle"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.planet.com/products/open-california/">
    <title>Planet — Open California</title>
    <dc:date>2019-02-21T22:11:08+00:00</dc:date>
    <link>https://www.planet.com/products/open-california/</link>
    <dc:creator>cschrader</dc:creator><dc:subject>temple capstone deeplearning tensorflow keras python remotesensing data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:5691b5bab0b3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://stackoverflow.com/questions/50065555/how-does-keras-imagedatagenerator-rescale-parameter-works">
    <title>python - How does Keras ImageDataGenerator rescale parameter works? - Stack Overflow</title>
    <dc:date>2019-02-21T22:10:57+00:00</dc:date>
    <link>https://stackoverflow.com/questions/50065555/how-does-keras-imagedatagenerator-rescale-parameter-works</link>
    <dc:creator>cschrader</dc:creator><dc:subject>temple capstone deeplearning tensorflow keras python remotesensing data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:7ce3703ea617/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://stackoverflow.com/questions/15345790/scipy-misc-module-has-no-attribute-imread">
    <title>python - scipy.misc module has no attribute imread? - Stack Overflow</title>
    <dc:date>2019-02-21T18:35:51+00:00</dc:date>
    <link>https://stackoverflow.com/questions/15345790/scipy-misc-module-has-no-attribute-imread</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[imread is deprecated in SciPy 1.0.0, and will be removed in 1.2.0. Use imageio.imread instead]]></description>
<dc:subject>python temple capstone</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:bde2b25f980d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/EKami/planet-amazon-deforestation">
    <title>EKami/planet-amazon-deforestation: The open source repository for the Kaggle Amazon forest devastation competition https://www.kaggle.com/c/planet-understanding-the-amazon-from-space</title>
    <dc:date>2019-02-19T22:56:14+00:00</dc:date>
    <link>https://github.com/EKami/planet-amazon-deforestation</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[The open source repository for the Kaggle Amazon forest devastation competition https://www.kaggle.com/c/planet-understanding-the-amazon-from-space]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras python remotesensing data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:f57a8522db1b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://gis.stackexchange.com/questions/279727/performing-deep-learning-land-cover-classification-using-r">
    <title>raster - Performing deep learning land cover classification using R? - Geographic Information Systems Stack Exchange</title>
    <dc:date>2019-02-19T22:54:30+00:00</dc:date>
    <link>https://gis.stackexchange.com/questions/279727/performing-deep-learning-land-cover-classification-using-r</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[The main reason that I am asking is because recently I found a few papers on Remote Sensing Image classification using Deep Learning and I was wondering if there were any R examples on that subject.

So, is there a code example, preferably a step-by-step, which I can use?]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras R remotesensing data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:33b9837da915/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:temple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:capstone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:remotesensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cschrader/t:data"/>
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</item>
<item rdf:about="https://www.reddit.com/r/MLQuestions/comments/9ml6fk/advice_for_satellite_image_classification_with/">
    <title>Advice for Satellite Image Classification with Keras in R : MLQuestions</title>
    <dc:date>2019-02-19T22:53:51+00:00</dc:date>
    <link>https://www.reddit.com/r/MLQuestions/comments/9ml6fk/advice_for_satellite_image_classification_with/</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[I am working on classifying satellite images of urban gardens. I want to do this in R and with Keras. I already have something put together from a class project, I will post pseudo-code below. I am looking for general advice and/or any input you have concerning the questions below. Thank you!]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras R remotesensing data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:a26f450ad0bd/</dc:identifier>
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</item>
<item rdf:about="https://github.com/zia207/Satellite-Images-Classification-with-Keras-R">
    <title>zia207/Satellite-Images-Classification-with-Keras-R: Deep Neural Network with keras-R (TensorFlow GUP backend): Satellite-Image Classification</title>
    <dc:date>2019-02-19T22:53:03+00:00</dc:date>
    <link>https://github.com/zia207/Satellite-Images-Classification-with-Keras-R</link>
    <dc:creator>cschrader</dc:creator><dc:subject>temple capstone deeplearning tensorflow keras R remotesensing data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:8b1d31263898/</dc:identifier>
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</item>
<item rdf:about="https://jkjung-avt.github.io/keras-inceptionresnetv2/">
    <title>Keras InceptionResNetV2</title>
    <dc:date>2019-02-19T22:26:35+00:00</dc:date>
    <link>https://jkjung-avt.github.io/keras-inceptionresnetv2/</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[In the post I’d like to show how easy it is to modify the code to use an even more powerful CNN model, ‘InceptionResNetV2’.]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras python remotesensing data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:b13224aa8d32/</dc:identifier>
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</item>
<item rdf:about="https://jkjung-avt.github.io/keras-tutorial/">
    <title>Keras Cats Dogs Tutorial</title>
    <dc:date>2019-02-19T22:26:12+00:00</dc:date>
    <link>https://jkjung-avt.github.io/keras-tutorial/</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[While there are many good examples online to get you started tackling image classification tasks using Keras, most of them are lacking in terms of how to take advantage of Keras’ built-in image augmentation functionalities to achieve best classification accuracy. Nonetheless, the following article on ‘The Keras Blog’ serves as a good starting point in that direction.]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras python remotesensing data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:b1eb8a0e11df/</dc:identifier>
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</item>
<item rdf:about="https://www.kaggle.com/crawford/deepsat-sat4">
    <title>DeepSat (SAT-4) Airborne Dataset | Kaggle</title>
    <dc:date>2019-02-19T22:07:50+00:00</dc:date>
    <link>https://www.kaggle.com/crawford/deepsat-sat4</link>
    <dc:creator>cschrader</dc:creator><dc:subject>temple capstone deeplearning tensorflow keras remotesensing data python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:88159acee6ac/</dc:identifier>
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</item>
<item rdf:about="https://www.kaggle.com/c/planet-understanding-the-amazon-from-space">
    <title>Planet: Understanding the Amazon from Space | Kaggle</title>
    <dc:date>2019-02-19T15:50:17+00:00</dc:date>
    <link>https://www.kaggle.com/c/planet-understanding-the-amazon-from-space</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[In this competition, Planet and its Brazilian partner SCCON are challenging Kagglers to label satellite image chips with atmospheric conditions and various classes of land cover/land use. Resulting algorithms will help the global community better understand where, how, and why deforestation happens all over the world - and ultimately how to respond.]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras remotesensing data python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:059196d4844d/</dc:identifier>
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</item>
<item rdf:about="https://github.com/chrieke/awesome-satellite-imagery-datasets">
    <title>chrieke/awesome-satellite-imagery-datasets: List of satellite imagery datasets with annotations for computer vision and deep learning</title>
    <dc:date>2019-02-19T15:45:25+00:00</dc:date>
    <link>https://github.com/chrieke/awesome-satellite-imagery-datasets</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[List of aerial and satellite imagery datasets with annotations for computer vision and deep learning. Newest datasets at the top of each category (Instance segmentation, object detection, semantic segmentation, chip classification, other).]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras R remotesensing data datasets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:31111356c1ab/</dc:identifier>
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</item>
<item rdf:about="http://weegee.vision.ucmerced.edu/datasets/landuse.html">
    <title>UC Merced Land Use Dataset</title>
    <dc:date>2019-02-19T15:24:56+00:00</dc:date>
    <link>http://weegee.vision.ucmerced.edu/datasets/landuse.html</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[This is a 21 class land use image dataset meant for research purposes.]]></description>
<dc:subject>temple capstone deeplearning tensorflow keras R remotesensing data datasets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:4a523dee382e/</dc:identifier>
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</item>
<item rdf:about="https://gis.stackexchange.com/questions/76919/is-it-possible-to-open-rasters-as-array-in-numpy-without-using-another-library">
    <title>python - Is it possible to open rasters as array in NumPy without using another library? - Geographic Information Systems Stack Exchange</title>
    <dc:date>2019-02-19T15:14:15+00:00</dc:date>
    <link>https://gis.stackexchange.com/questions/76919/is-it-possible-to-open-rasters-as-array-in-numpy-without-using-another-library</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[ know that it is possible to open a raster as an array in NumPy using GDAL, but I want to skip GDAL and use NumPy only, as it is cooler handling rasters with NumPy as matrices. There is a similar question here: but the answer solutions involve using other librarie]]></description>
<dc:subject>python gis temple programming</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:9b05010e2037/</dc:identifier>
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</item>
<item rdf:about="https://medium.com/tensorflow/whats-coming-in-tensorflow-2-0-d3663832e9b8?linkId=62351082">
    <title>What’s coming in TensorFlow 2.0 – TensorFlow – Medium</title>
    <dc:date>2019-02-15T23:52:00+00:00</dc:date>
    <link>https://medium.com/tensorflow/whats-coming-in-tensorflow-2-0-d3663832e9b8?linkId=62351082</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[What’s coming in TensorFlow 2.0



#TensorFlow #ai #DeepLearning #MachineLearning… ]]></description>
<dc:subject>ai DeepLearning TensorFlow MachineLearning fromtwitter_favs</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:2326c6329330/</dc:identifier>
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</item>
<item rdf:about="https://keras.rstudio.com/articles/getting_started.html">
    <title>Getting Started with Keras • keras</title>
    <dc:date>2019-02-14T18:49:12+00:00</dc:date>
    <link>https://keras.rstudio.com/articles/getting_started.html</link>
    <dc:creator>cschrader</dc:creator><dc:subject>R tensorflow datascience deeplearning temple keras python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cschrader/b:3be3faf85403/</dc:identifier>
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</item>
<item rdf:about="https://qualityandinnovation.com/2017/10/16/a-newbies-install-of-keras-tensorflow-on-windows-10-with-r/">
    <title>A Newbie’s Install of Keras &amp; Tensorflow on Windows 10 with R | Quality and Innovation</title>
    <dc:date>2019-02-14T18:49:00+00:00</dc:date>
    <link>https://qualityandinnovation.com/2017/10/16/a-newbies-install-of-keras-tensorflow-on-windows-10-with-r/</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[Here are the steps I used to get things running on Windows 10, leveraging clues in about 15 different online resources — and yes (I found out the hard way), the order of operations is very important. I do not claim to have nailed the order of operations here, but definitely one that works.

]]></description>
<dc:subject>R tensorflow datascience deeplearning temple keras python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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</item>
<item rdf:about="https://keras.rstudio.com/">
    <title>R Interface to 'Keras' • keras</title>
    <dc:date>2019-02-12T22:40:16+00:00</dc:date>
    <link>https://keras.rstudio.com/</link>
    <dc:creator>cschrader</dc:creator><dc:subject>temple capstone deeplearning tensorflow keras R</dc:subject>
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<item rdf:about="https://ai.googleblog.com/2016/08/improving-inception-and-image.html">
    <title>Google AI Blog: Improving Inception and Image Classification in TensorFlow</title>
    <dc:date>2019-02-12T22:40:03+00:00</dc:date>
    <link>https://ai.googleblog.com/2016/08/improving-inception-and-image.html</link>
    <dc:creator>cschrader</dc:creator><dc:subject>temple capstone deeplearning tensorflow google</dc:subject>
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<item rdf:about="https://github.com/tensorflow/models/blob/master/research/slim/README.md">
    <title>models/README.md at master · tensorflow/models · GitHub</title>
    <dc:date>2019-02-12T22:39:53+00:00</dc:date>
    <link>https://github.com/tensorflow/models/blob/master/research/slim/README.md</link>
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</item>
<item rdf:about="https://github.com/harsha2010/magellan">
    <title>GitHub - harsha2010/magellan: Geo Spatial Data Analytics on Spark</title>
    <dc:date>2018-11-15T16:25:35+00:00</dc:date>
    <link>https://github.com/harsha2010/magellan</link>
    <dc:creator>cschrader</dc:creator><dc:subject>temple todo python gis spark</dc:subject>
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<item rdf:about="https://hortonworks.com/blog/magellan-geospatial-analytics-in-spark/">
    <title>Magellan: Geospatial Analytics on Spark - Hortonworks</title>
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    <link>https://hortonworks.com/blog/magellan-geospatial-analytics-in-spark/</link>
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<item rdf:about="https://gis.stackexchange.com/questions/163445/getting-topologyexception-input-geom-1-is-invalid-which-is-due-to-self-intersec#163480">
    <title>polygon - Getting TopologyException: Input geom 1 is invalid which is due to self-intersection in R? - Geographic Information Systems Stack Exchange</title>
    <dc:date>2018-11-14T02:18:26+00:00</dc:date>
    <link>https://gis.stackexchange.com/questions/163445/getting-topologyexception-input-geom-1-is-invalid-which-is-due-to-self-intersec#163480</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[Using a zero-width buffer cleans up many topology problems in R.

spydf_states <- gBuffer(spydf_states, byid=TRUE, width=0)
]]></description>
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<item rdf:about="https://certbot-dns-linode.readthedocs.io/en/stable/#">
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<item rdf:about="https://devops.stackexchange.com/questions/3757/how-to-install-certbot-plugins">
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    <link>https://devops.stackexchange.com/questions/3757/how-to-install-certbot-plugins</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[pip install certbot-dns]]></description>
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<item rdf:about="https://scihub.copernicus.eu/dhus/#/home">
    <title>[untitled]</title>
    <dc:date>2018-10-11T18:22:10+00:00</dc:date>
    <link>https://scihub.copernicus.eu/dhus/#/home</link>
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<item rdf:about="https://gisgeography.com/free-satellite-imagery-data-list/">
    <title>15 Free Satellite Imagery Data Sources - GIS Geography</title>
    <dc:date>2018-10-11T18:22:02+00:00</dc:date>
    <link>https://gisgeography.com/free-satellite-imagery-data-list/</link>
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<item rdf:about="https://www.asprs.org/Students.html">
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    <link>https://www.asprs.org/Students.html</link>
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<item rdf:about="https://www.newsweek.com/equal-earth-map-continents-accurate-2d-1102404">
    <title>New World Map That Accurately Shows Earth in 2D Created by Scientists</title>
    <dc:date>2018-10-08T14:57:56+00:00</dc:date>
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<item rdf:about="https://www.onetonline.org/link/summary/19-2099.01">
    <title>19-2099.01 - Remote Sensing Scientists and Technologists</title>
    <dc:date>2018-10-04T02:44:29+00:00</dc:date>
    <link>https://www.onetonline.org/link/summary/19-2099.01</link>
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</rdf:Bag></taxo:topics>
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<item rdf:about="https://www.ursaspace.com/careers">
    <title>Ursa | Careers</title>
    <dc:date>2018-10-04T02:30:41+00:00</dc:date>
    <link>https://www.ursaspace.com/careers</link>
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<item rdf:about="https://www.indeed.com/viewjob?jk=1cc1e8d3518e6082&amp;tk=1coue7gc5ah95802&amp;from=serp&amp;vjs=3">
    <title>Product Manager - King of Prussia, PA - Indeed.com</title>
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<item rdf:about="http://prj2epsg.org/search">
    <title>PRJ 2 EPSG</title>
    <dc:date>2018-10-03T22:35:28+00:00</dc:date>
    <link>http://prj2epsg.org/search</link>
    <dc:creator>cschrader</dc:creator><description><![CDATA[Prj2EPSG is a simple service for converting well-known text projection information from .prj files into standard EPSG codes.]]></description>
<dc:subject>gis temple postfix postgis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://jupyter.org/install.html">
    <title>Project Jupyter | Installing the Jupyter Notebook</title>
    <dc:date>2018-08-15T23:36:34+00:00</dc:date>
    <link>https://jupyter.org/install.html</link>
    <dc:creator>cschrader</dc:creator><dc:subject>projects learning python jupyter</dc:subject>
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<item rdf:about="https://www.dataquest.io/">
    <title>Learn Data Science With Python And R Projects | Dataquest</title>
    <dc:date>2018-08-15T23:30:48+00:00</dc:date>
    <link>https://www.dataquest.io/</link>
    <dc:creator>cschrader</dc:creator><dc:subject>programming learning datascience data python R</dc:subject>
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