Pixies - Hear Me Out
Early in the evening I watched the Pixies' official video of "Hear me Out". I am not so sure about the song, but I do like the video a lot. Let's hope a new album is in the making!
How the CPython compiler works
In the first post of the series we've looked at the CPython VM. We've learned that it works by executing a series of instructions called bytecode. We've also seen that Python bytecode is not sufficient to fully describe what a piece of code does. That's why there exists a notion of a code object. To execute a code block such as a module or a function means to execute a corresponding code object. A code object contains block's bytecode, constants and names of variables used in the block and block's various properties.
Typically, a Python programmer doesn't write bytecode and doesn't create the code objects but writes a normal Python code. So CPython must be able to create a code object from a source code. This job is done by the CPython compiler. In this part we'll explore how it works.
Source: Python behind the scenes #2: how the CPython compiler works, an article by Victor Skvortsov.
Higher Order Functions in Python
Also known as first-class functions, functions can take other functions as parameters and also return other functions.
Since everything in Python is an object, we can treat functions as such.
Source: Higher Order Functions in Python, an article by Renan Moura.
Image Super-Resolution: A Comprehensive Review
Image Super Resolution refers to the task of enhancing the resolution of an image from low-resolution (LR) to high (HR). It is popularly used in the following applications:
- Surveillance: to detect, identify, and perform facial recognition on low-resolution images obtained from security cameras.
- Medical: capturing high-resolution MRI images can be tricky when it comes to scan time, spatial coverage, and signal-to-noise ratio (SNR). Super resolution helps resolve this by generating high-resolution MRI from otherwise low-resolution MRI images.
- Media: super resolution can be used to reduce server costs, as media can be sent at a lower resolution and upscaled on the fly.
Deep learning techniques have been fairly successful in solving the problem of image and video super-resolution. In this article we will discuss the theory involved, various techniques used, loss functions, metrics, and relevant datasets. You can also run the code for one of the models we'll cover, ESPCN, for free on the ML Showcase.
Source: A Review of Image Super-Resolution, an article by Anil Chandra Naidu Matcha.