Programming: Python, Node.js and LLVM
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uwsgi weirdness with --http
Instead of upgrading everything on my server, I'm just starting from scratch. From Ubuntu 16.04 to Ubuntu 19.04 and I also upgraded everything else in sight. One of them was uwsgi. I copied various user config files but for uwsgi things didn't very well. On the old server I had uwsgi version 2.0.12-debian and on the new one 2.0.18-debian. The uWSGI changelog is pretty hard to read but I sure don't see any mention of this.
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Wingware Blog: Viewing Arrays and Data Frames in Wing Pro 7
Wing Pro 7 introduced an array and data frame viewer that can be used to inspect data objects in the debugger. Values are transferred to the IDE according to what portion of the data is visible on the screen, so working with large data sets won't slow down the IDE.
The array viewer works with Pandas, numpy, sqlite3, xarray, Python's builtin lists, tuples, and dicts, and other classes that emulate lists, tuples, or dicts.
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Solving Sequence Problems with LSTM in Keras: Part 2
This is the second and final part of the two-part series of articles on solving sequence problems with LSTMs. In the part 1 of the series, I explained how to solve one-to-one and many-to-one sequence problems using LSTM. In this part, you will see how to solve one-to-many and many-to-many sequence problems via LSTM in Keras.
Image captioning is a classic example of one-to-many sequence problems where you have a single image as input and you have to predict the image description in the form of a word sequence. Similarly, stock market prediction for the next X days, where input is the stock price of the previous Y days, is a classic example of many-to-many sequence problems.
In this article you will see very basic examples of one-to-many and many-to-many problems. However, the concepts learned in this article will lay the foundation for solving advanced sequence problems, such as stock price prediction and automated image captioning that we will see in the upcoming articles.
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Voronoi Mandalas
I started with Carlos Focil's mandalapy code, modifying the parameters until I had a design I liked. I decided to make the Voronoi diagram show both points and vertices, and I gave it an equal aspect ratio. Carlos' mandalapy code is a port of Antonio Sánchez Chinchón's inspiring work drawing mandalas with R, using the deldir library to plot Voronoi tesselations.
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Python Code Kata: Fizzbuzz
A code kata is a fun way for computer programmers to practice coding. They are also used a lot for learning how to implement Test Driven Development (TDD) when writing code. One of the popular programming katas is called FizzBuzz. This is also a popular interview question for computer programmers.
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why python is the best-suited programming language machine learning
Machine Learning is the hottest trend in modern times. According to Forbes, Machine learning patents grew at a 34% rate between 2013 and 2017 and this is only set to increase in the future. And Python is the primary programming language used for much of the research and development in Machine Learning. So much so that Python is the top programming language for Machine Learning according to Github.
Python is currently the most popular programming language for research and development in Machine Learning. But you don’t need to take my word for it! According to Google Trends, the interest in Python for Machine Learning has spiked to an all-new high with other ML languages such as R, Java, Scala, Julia, etc. lagging far behind.
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Node.js now available in Haiku
As some have already known for a long time, many platforms have had support for writing software in JavaScript or TypeScript with the help of the Node.js runtime and over the years, much of the software written by developers these days have gradually been written in either of those languages. However, Haiku has lacked a Node.js port for quite sometime and it wasn’t possible to run or develop JavaScript based software or libraries that depended on the Node.js runtime. Now I can say that Node.js is available for Haiku and can be downloaded from HaikuDepot on 64 bit (32 bit support is being worked on). The version which is currently available is 12.3.1 and is already being updated to the latest version at the time of this writing to 12.10.0 and support for the upcoming LTS version is also coming to HaikuPorts. Several patches have been upstreamed by members of the HaikuPorts team to projects such as libuv (cross-platform async I/O library), GN, etc and we hope to upstream to larger projects like V8 (Google’s JavaScript engine used in Chromium and QtWebEngine) and the Node.js project, which will ease the bringup of a future Node LTS release for Haiku.
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Node.js Brought To BeOS-Inspired Haiku Open-Source OS
Haiku as the open-source operating system that still maintains BeOS compatibility continues tacking on modern features and support for software well past the days of BeOS.
The newest major piece of software working on BeOS is Node.js, including support for its NPM package manager.
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LLVM 9.0 Released With Ability To Build The Linux x86_64 Kernel, Experimental OpenCL C++
It's coming almost one month behind schedule, but LLVM 9.0 is out today along with the Clang 9.0 C/C++ compiler and associated sub-projects for this open-source compiler infrastructure.
LLVM 9.0 is an exciting release with bringing the ability to build the mainline Linux x86_64 kernel using LLVM/Clang 9.0 now that "asm goto" support was finally added. The AArch64 support was in better standing previously but now at long last the mainline Clang 9.0 compiler can build the current Linux kernel releases with not needing any extra patches on either side, just point the kernel build CC to Clang.
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