Showmedo is proud to publish:[1452] Closing remarks and using dictionaries effectively for scientific computing, a video-tutorial by unpingco This video is part of the series Scientific and parallel computing using IPython

In this final segment, we highlight concepts important for new scientific Python programmers, especially those coming from a Matlab environment. We discuss effective use [...]

Showmedo is proud to publish:[1451] Garbage Collection in Python – Part II -, a video-tutorial by gasto This video is part of the series Python from zero.

How to instantiate the previously created class ( Foo ) is shown, using IDLE (the Integrated Development Environment , that comes with the standard CPython distribution). 2 referrers [...]

Showmedo is proud to publish:[1439] Case Study: Robust Regression Methods Using Python and Parallel IPython, a video-tutorial by unpingco This video is part of the series Scientific and parallel computing using IPython

In this segment, we continue with the case study of robust regression methods, but here we show how to use generators and the [...]

Showmedo is proud to publish:[1438] Case Study: Robust Regression Methods Using Python continued, a video-tutorial by unpingco This video is part of the series Scientific and parallel computing using IPython

In this segment, we discuss how the least-median estimate can be computed from the sample dataset using generators.

Showmedo is proud to publish:[1437] Case Study: Robust Regression Methods Using Python , a video-tutorial by unpingco This video is part of the series Scientific and parallel computing using IPython

In this case study, we pull together everything we have learned so far in order to compute the least MEDIAN squared estimate of a line [...]

Showmedo is proud to publish:[1435] Finer Control for Parallel Computing in IPython, a video-tutorial by unpingco This video is part of the series Scientific and parallel computing using IPython

In this segment, we discuss the push, pull, and push_function, features of IPython that allow finer control over individual namespaces of remote nodes and over the [...]

Showmedo is proud to publish:[1434] Distributing Parallel Computations Using IPython, a video-tutorial by unpingco This video is part of the series Scientific and parallel computing using IPython

In this segment, we discuss the map feature to distribute computations across the available nodes, how to use targets to specify which nodes to use, and how to [...]

Showmedo is proud to publish:[1433] Basic Parallel Computing Using IPython, a video-tutorial by unpingco This video is part of the series Scientific and parallel computing using IPython

In this segment, we discuss cover importing modules for the remote nodes and how to distribute data (i.e. scatter, gather) to and from these nodes.

Showmedo is proud to publish:[1432] Getting Started with Parallel IPython, a video-tutorial by unpingco This video is part of the series Scientific and parallel computing using IPython

IPython provides excellent interactive parallel computing tools. In this segment, we discuss how to set up remote computational engines using ipcluster and how to use special magic [...]

Showmedo is proud to publish:[1431] Garbage Collection in Python – Part I -, a video-tutorial by gasto This video is part of the series Python from zero.

We view an animated illustration of how the garbage collector collects objects under Python 3.0 . That is, two referrers are created by being associated to the Foo [...]

Showmedo is proud to publish:[1435] Finer Control for Parallel Computing in IPython, a video-tutorial by unpingco This video is part of the series Scientific and parallel computing using IPython

In this segment, we discuss the push, pull, and push_function, features of IPython that allow finer control over individual namespaces of remote nodes and over the [...]

Showmedo is proud to publish:[1433] Basic Parallel Computing Using IPython, a video-tutorial by unpingco This video is part of the series Scientific and parallel computing using IPython

In this segment, we discuss cover importing modules for the remote nodes and how to distribute data (i.e. scatter, gather) to and from these nodes.

Showmedo is proud to publish:[1432] Getting Started with Parallel IPython, a video-tutorial by unpingco This video is part of the series Scientific and parallel computing using IPython

IPython provides excellent interactive parallel computing tools. In this segment, we discuss how to set up remote computational engines using ipcluster and how to use special magic [...]

Showmedo is proud to publish:[1431] Garbage Collection in Python – Part I -, a video-tutorial by gasto This video is part of the series Python from zero.

We view an animated illustration of how the garbage collector collects objects under Python 3.0 . That is, two referrers are created by being associated to the Foo [...]

Showmedo is proud to publish:[1430] Getting started with Matplotlib, a video-tutorial by unpingco This video is part of the series Scientific and parallel computing using IPython

Matplotlib is one of the best, if not the best, plotting package for scientific Python. In this segment, we discuss how to best use the Matplotlib website to get [...]

Showmedo is proud to publish:[1413] Introducing Sympy — Symbolic Mathematics within IPython, a video-tutorial by unpingco This video is part of the series Scientific and parallel computing using IPython

Introducing Sympy for symbolic mathematics within IPython. Basic operations and advice for Windows users.

Showmedo is proud to publish:[1412] Profiling in IPython, a video-tutorial by unpingco This video is part of the series Scientific and parallel computing using IPython

Basic profiling methods available in IPython.

Showmedo is proud to publish:[1406] Using the IPython Library, a video-tutorial by unpingco This video is part of the series Visual Parallel Computing Using Python-based VISION/HPC

This segment discusses the MEC and MECXLocal nodes in the IPython library to quickly prototype a parallel computation without having a prior connection to a backend cluster.