Tutorial: working with missing data in Python
http://nbviewer.jupyter.org/github/ResidentMario/python-missing-data/blob/master/missing-data.ipynb
/r/pystats
https://redd.it/4eh4va
http://nbviewer.jupyter.org/github/ResidentMario/python-missing-data/blob/master/missing-data.ipynb
/r/pystats
https://redd.it/4eh4va
BuzzFeedNews/Detecting Match Fixing in Tennis Matches (Open Data Journalism)
https://github.com/BuzzFeedNews/2016-01-tennis-betting-analysis/blob/master/notebooks/tennis-analysis.ipynb
/r/JupyterNotebooks
https://redd.it/4d2ouo
https://github.com/BuzzFeedNews/2016-01-tennis-betting-analysis/blob/master/notebooks/tennis-analysis.ipynb
/r/JupyterNotebooks
https://redd.it/4d2ouo
GitHub
2016-01-tennis-betting-analysis/tennis-analysis.ipynb at master · BuzzFeedNews/2016-01-tennis-betting-analysis
Methodology and code supporting the BuzzFeed News/BBC article, "The Tennis Racket," published Jan. 17, 2016. - 2016-01-tennis-betting-analysis/tennis-analysis.ipynb at master · Bu...
JupyterLab: the next generation of the Jupyter Notebook
http://blog.jupyter.org/2016/07/14/jupyter-lab-alpha/
/r/IPython
https://redd.it/4szoix
http://blog.jupyter.org/2016/07/14/jupyter-lab-alpha/
/r/IPython
https://redd.it/4szoix
Project Jupyter
JupyterLab: the next generation of the Jupyter Notebook
Learning the lessons of the Jupyter Notebook It's been a long time in the making, but today we want to start engaging our community with an early (pre-alpha) release of the next generation of the Jupyter Notebook application, which we are calling JupyterLab.…
Kalman and Bayesian Filters in Python
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
/r/JupyterNotebooks
https://redd.it/4aw2wh
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
/r/JupyterNotebooks
https://redd.it/4aw2wh
GitHub
GitHub - rlabbe/Kalman-and-Bayesian-Filters-in-Python: Kalman Filter book using Jupyter Notebook. Focuses on building intuition…
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filt...
What's everyone working on this week?
Tell /r/python what you're working on this week! You can be bragging, grousing, sharing your passion, or explaining your pain. Talk about your current project or your pet project; whatever you want to share.
/r/Python
https://redd.it/5jdewv
Tell /r/python what you're working on this week! You can be bragging, grousing, sharing your passion, or explaining your pain. Talk about your current project or your pet project; whatever you want to share.
/r/Python
https://redd.it/5jdewv
reddit
What's everyone working on this week? • /r/Python
Tell /r/python what you're working on this week! You can be bragging, grousing, sharing your passion, or explaining your pain. Talk about your...
[D] What are current relations between (algebraic) topology and deep learning?
I am a graduate student in AI with an undergrad in pure math (algebraic topology, abstract algebra, PDEs). Currently I am working a lot on deep learning - especially manifold learning and nonlinear embeddings. Since finding a mapping on a nonlinear sub manifold and the normally unknown network architecture are closely linked I asked myself, if there is a connection between the topological properties of the map/diffeomorphism generated by a DNN and it's capabilities to learn certain things. I found this introduction on colah's blog and I also started to read on the methodology of Gunnar Carlson's topological data analysis. However, colah's blog gives me the idea where the journey could head to and Carlson's way is not really connected to research on DNN (in terms of learning diffeomorphism of your data) - as far as I see it. But I am stuck finding deeper literature on that. Can you give any recommendation for papers or introductory work on this boundary? Everything bringing together the topology of neural networks, group theory and algebraization (e.g. using functors such as chain complexes or homotopy) would really interest me.
/r/MachineLearning
https://redd.it/5jfaox
I am a graduate student in AI with an undergrad in pure math (algebraic topology, abstract algebra, PDEs). Currently I am working a lot on deep learning - especially manifold learning and nonlinear embeddings. Since finding a mapping on a nonlinear sub manifold and the normally unknown network architecture are closely linked I asked myself, if there is a connection between the topological properties of the map/diffeomorphism generated by a DNN and it's capabilities to learn certain things. I found this introduction on colah's blog and I also started to read on the methodology of Gunnar Carlson's topological data analysis. However, colah's blog gives me the idea where the journey could head to and Carlson's way is not really connected to research on DNN (in terms of learning diffeomorphism of your data) - as far as I see it. But I am stuck finding deeper literature on that. Can you give any recommendation for papers or introductory work on this boundary? Everything bringing together the topology of neural networks, group theory and algebraization (e.g. using functors such as chain complexes or homotopy) would really interest me.
/r/MachineLearning
https://redd.it/5jfaox
reddit
[D] What are current relations between... • /r/MachineLearning
I am a graduate student in AI with an undergrad in pure math (algebraic topology, abstract algebra, PDEs). Currently I am working a lot on deep...
Top 10 Python libraries of 2016
https://tryolabs.com/blog/2016/12/20/top-10-python-libraries-of-2016/
/r/Python
https://redd.it/5jf64k
https://tryolabs.com/blog/2016/12/20/top-10-python-libraries-of-2016/
/r/Python
https://redd.it/5jf64k
Tryolabs
Top 10 Python libraries of 2016
JupyterLab Building Blocks for Interactive Computing | SciPy 2016 | Brian Granger
https://www.youtube.com/watch?v=Ejh0ftSjk6g
/r/IPython
https://redd.it/4t0due
https://www.youtube.com/watch?v=Ejh0ftSjk6g
/r/IPython
https://redd.it/4t0due
YouTube
JupyterLab: Building Blocks for Interactive Computing | SciPy 2016 | Brian Granger
Project Jupyter provides building blocks for interactive and exploratory computing. These building blocks make science and data science reproducible across o...
Statistics for Software: Instrumenting Code for Reliability and Performance
https://www.paypal-engineering.com/2016/04/11/statistics-for-software/
/r/pystats
https://redd.it/4ecj6q
https://www.paypal-engineering.com/2016/04/11/statistics-for-software/
/r/pystats
https://redd.it/4ecj6q
Solving a nonlinear ordinary differential equation
http://nbviewer.jupyter.org/github/Jabberwockyll/NumericalODE/blob/master/numerical_ode.ipynb
/r/JupyterNotebooks
https://redd.it/4awf63
http://nbviewer.jupyter.org/github/Jabberwockyll/NumericalODE/blob/master/numerical_ode.ipynb
/r/JupyterNotebooks
https://redd.it/4awf63
nbviewer.jupyter.org
Notebook on nbviewer
Check out this Jupyter notebook!
Jupyter Events Calendar
This calendar contains the dates and locations of Jupyter Developer talks and workshops.
[Jupyter Events](https://calendar.google.com/calendar/embed?src=p51j0ac1iccmj44tae12hq4dk0%40group.calendar.google.com&ctz=America/Los_Angeles)
/r/IPython
https://redd.it/4t2g9y
This calendar contains the dates and locations of Jupyter Developer talks and workshops.
[Jupyter Events](https://calendar.google.com/calendar/embed?src=p51j0ac1iccmj44tae12hq4dk0%40group.calendar.google.com&ctz=America/Los_Angeles)
/r/IPython
https://redd.it/4t2g9y
Causal Discovery Software Available for Big Data Analysis
The Center for Causal Discovery (CCD) (www.ccd.pitt.edu) has released the Fast Greedy Search (FGS) algorithm (an optimized version of Chickering's Greedy Equivalence Search algorithm) for use by biomedical investigators who are searching for causal associations in large sets of continuous data. A technical paper describing the optimization is available at http://arxiv.org/abs/1507.07749. It is available as free and open source software. This release is just the first step toward providing a suite of algorithms that will assist biomedical researchers in analyzing their data to obtain causal insights.
Using simulated data, FGS was able to learn a causal network on data containing 50,000 variables and 1,000 samples in about 15 minutes on a laptop computer. While FGS does not model hidden variables that cause two or more measured variables, an upcoming release of another algorithm will do so.
FGS is available as a command line implementation (Causal-cmd) that calls a local Java library or as a Java web application (Causal-web) that runs the analysis at the Pittsburgh Supercomputing Center; the API’s can also be run through R (R-causal) or Python (Py-causal). Additional details and instructions for downloading both these versions of the software are available at
http://www.ccd.pitt.edu/wiki/index.php?title=Tools_and_Software.
Our goal is to help the biomedical community use causal modeling to gain novel insights and drive innovative research, so we hope to make these tools as usable and useful as possible. We welcome any and all feedback that you might have, which will help us improve this and future releases.
/r/pystats
https://redd.it/4cqctx
The Center for Causal Discovery (CCD) (www.ccd.pitt.edu) has released the Fast Greedy Search (FGS) algorithm (an optimized version of Chickering's Greedy Equivalence Search algorithm) for use by biomedical investigators who are searching for causal associations in large sets of continuous data. A technical paper describing the optimization is available at http://arxiv.org/abs/1507.07749. It is available as free and open source software. This release is just the first step toward providing a suite of algorithms that will assist biomedical researchers in analyzing their data to obtain causal insights.
Using simulated data, FGS was able to learn a causal network on data containing 50,000 variables and 1,000 samples in about 15 minutes on a laptop computer. While FGS does not model hidden variables that cause two or more measured variables, an upcoming release of another algorithm will do so.
FGS is available as a command line implementation (Causal-cmd) that calls a local Java library or as a Java web application (Causal-web) that runs the analysis at the Pittsburgh Supercomputing Center; the API’s can also be run through R (R-causal) or Python (Py-causal). Additional details and instructions for downloading both these versions of the software are available at
http://www.ccd.pitt.edu/wiki/index.php?title=Tools_and_Software.
Our goal is to help the biomedical community use causal modeling to gain novel insights and drive innovative research, so we hope to make these tools as usable and useful as possible. We welcome any and all feedback that you might have, which will help us improve this and future releases.
/r/pystats
https://redd.it/4cqctx
Practical Deep Learning For Coders—18 hours of lessons for free
http://course.fast.ai/
/r/Python
https://redd.it/5jgv98
http://course.fast.ai/
/r/Python
https://redd.it/5jgv98
Practical Deep Learning for Coders
Practical Deep Learning for Coders - Practical Deep Learning
A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.
Why class-based views are not all that great
https://medium.com/@patrys/why-class-based-views-are-not-all-that-great-4b3202f38309
/r/django
https://redd.it/5jgcf6
https://medium.com/@patrys/why-class-based-views-are-not-all-that-great-4b3202f38309
/r/django
https://redd.it/5jgcf6
Medium
Why class-based views are not all that great
Disclaimer: the views and opinions presented here are mine and do not reflect those of my employer, Mirumee Software.
Using R with Jupyter Notebooks running a Python Kernel
http://www.rittmanmead.com/2016/07/using-r-jupyter-notebooks-big-data-discovery/
/r/IPython
https://redd.it/4stbeg
http://www.rittmanmead.com/2016/07/using-r-jupyter-notebooks-big-data-discovery/
/r/IPython
https://redd.it/4stbeg
Rittman Mead
Using R with Jupyter Notebooks and Oracle Big Data Discovery
Oracle's Big Data Discovery encompasses a good amount of exploration, transformation, and visualisation capabilities for datasets residing in your organisation’s data reservoir. Even with this though, there may come a time when your data scientists want to…
Recycle Python Program
I am still in the mist of learning python programming and I wanted to create a little program. This program basically is an alternative to the "rm" command we love and hate on our linux/unix based systems. The program instead sends things to the recycle/trash bin. I am still trying to expand on the functionality of the program but I do hope you all can check it out and maybe test it out a bit and let me know what you think. Something like this may exist already but I guess I just wanted to create my first "why isn't there..." based program.
I created it on my my mac, and I think the program will work on linux too. I had issues getting the install script to run on systems that use "yum" package manager. I tried to automate it all, but honestly all you need to run the program is python3 and send2trash module using pip3. Would love to have suggestions on improving the code to make it as elegant as possible.
https://github.com/tarrell13/Recycle
/r/Python
https://redd.it/5jib2h
I am still in the mist of learning python programming and I wanted to create a little program. This program basically is an alternative to the "rm" command we love and hate on our linux/unix based systems. The program instead sends things to the recycle/trash bin. I am still trying to expand on the functionality of the program but I do hope you all can check it out and maybe test it out a bit and let me know what you think. Something like this may exist already but I guess I just wanted to create my first "why isn't there..." based program.
I created it on my my mac, and I think the program will work on linux too. I had issues getting the install script to run on systems that use "yum" package manager. I tried to automate it all, but honestly all you need to run the program is python3 and send2trash module using pip3. Would love to have suggestions on improving the code to make it as elegant as possible.
https://github.com/tarrell13/Recycle
/r/Python
https://redd.it/5jib2h
GitHub
tarrell13/Recycle
Recycle - Command line program used to send documents to the Trash Bin instead of deleting.
Image Processing 101
http://nbviewer.jupyter.org/github/piratefsh/image-processing-101/blob/master/Image%20Processing%20101.ipynb
/r/JupyterNotebooks
https://redd.it/4aeiyr
http://nbviewer.jupyter.org/github/piratefsh/image-processing-101/blob/master/Image%20Processing%20101.ipynb
/r/JupyterNotebooks
https://redd.it/4aeiyr
New contest: make real life IoT projects using Python and microcontrollers
http://www.zerynth.com/blog/make-real-life-iot-projects-using-python-and-microcontrollers/
/r/Python
https://redd.it/5jjfro
http://www.zerynth.com/blog/make-real-life-iot-projects-using-python-and-microcontrollers/
/r/Python
https://redd.it/5jjfro
Zerynth - Python for Microcontrollers, IoT and Embedded Solutions
Make Real Life IoT projects using Python and Microcontrollers
IoT is potentially one of the most important trends in the history of the industry. But it’s time to move beyond "potential.” It’s time to make IoT real!
nbextension: HTML WYSIWYG editor for Markdown/HTML cells
https://github.com/genepattern/jupyter-wysiwyg
/r/IPython
https://redd.it/4sp3rv
https://github.com/genepattern/jupyter-wysiwyg
/r/IPython
https://redd.it/4sp3rv
GitHub
genepattern/jupyter-wysiwyg
A WYSIWYG markdown/HTML editor for Jupyter Notebook - genepattern/jupyter-wysiwyg