an #IPython notebook for histogram filters, similar to #particle-filters
on 02023-10-07with "Babel" #Emacs #org-mode supports code blocks with inline output, similar to #Jupyter or the #IPython #notebook. Examples include Ruby, Ditaa, R, sh, Python, and elisp, with inputs coming from org-mode tables or other code blocks.
on 02023-08-09how to invoke the pdb #debugger when scripting #GDB in #Python: python pdb.run('gdb.execute("print $1[0]")'). Also you can start up an #IPython kernel inside GDB with IPython.embed_kernel() (not sure if this works for notebooks) and define a function in .gdbinit that invokes Python by saying define functionname ... end with a python ... end block inside it.
discussion of #IPython/Jupyter notebooks and their problems
on 02018-08-30Zeppelin looks like an alternative to #IPython/Jupyter? A notebook interface with Spark, SQL and Scala
on 02017-07-13how to use #IPython/#Jupyter with #SymPy to do symbolic #math in a convenient notebook format.
on 02016-10-11client-side rendering of #IPython or jupyter notebooks in js
on 02016-09-23a quick-start post for doing #neural-networks with #IPython; not sure which NN library they’re using
on 02016-07-12#Norvig’s more or less active-essay explanation of basic #economics, using simulations with #matplotlib and #IPython.
on 02015-11-19"TensorFlow" is a high-throughput #dataflow array computing library and IDE with built-in reverse-mode #automatic-differentiation for optimization, with Python and C++ APIs and #IPython integration. At this moment in history, the growth of computer power has made a bunch of important #DSP and statistical tasks just feasible, so we are seeing things like self-driving cars, superhuman image recognition, and so on. But it’s been very difficult to take advantage of the available computational power, because it’s in the form of GPUs and clusters. So this is designed to make it easy to do exactly these things, and to scale them with your available computing power, along with libraries of the latest tricks in neural networks, machine learning (which is pretty close to "statistics").
on 02015-11-09Markov asset pricing using Numpy in #Python and #IPython, part of “a series of lectures on quantitative economic modelling, designed and written by Thomas J. Sargent and John Stachurski.”
on 02015-08-26An #ebook on #Kalman-filters as a series of #IPython notebooks that does mention #particle-filters a bit. #cc-by.
on 02015-08-12#Guix has #IPython as of 0.8.1, January 2015. #Nix
on 02015-08-06How to do nice #plotting of financial data with #IPython, #matplotlib, and #numpy (apparently matplotlib has a fetch_historical_yahoo function too now!)
Wes McKinney’s #pandas tour #IPython notebook from his 10-minute Pandas demo video.
on 02015-08-05