Maaz Ullah maazullah96

Chat GPT "DAN" (and other "Jailbreaks")

GROKKING NOTES

I liked the way Grokking the coding interview organized problems into learnable patterns. However, the course is expensive and the majority of the time the problems are copy-pasted from leetcode. As the explanations on leetcode are usually just as good, the course really boils down to being a glorified curated list of leetcode problems.

So below I made a list of leetcode problems that are as close to grokking problems as possible.

Pattern: Sliding Window

Youtube channels:

Convert PascalVOC Annotations to YOLO

This script reads PascalVOC xml files, and converts them to YOLO txt files.

Note: This script was written and tested on Ubuntu. YMMV on other OS's.

Disclaimer: This code is a modified version of Joseph Redmon's voc_label.py

Instructions:

Place the convert_voc_to_yolo.py file into your data folder.

Interview Questions

Node.js

Q1: What do you mean by Asynchronous API? ☆☆

Answer: All APIs of Node.js library are aynchronous that is non-blocking. It essentially means a Node.js based server never waits for a API to return data. Server moves to next API after calling it and a notification mechanism of Events of Node.js helps server to get response from the previous API call.

Source: tutorialspoint.com

Install OpenCV 4.10 with CUDA 12.6 and CUDNN 8.9 in Ubuntu 24.04

First of all install update and upgrade your system:

    $ sudo apt update
    $ sudo apt upgrade

Then, install required libraries:

	import cv2
	import time

	CONFIDENCE_THRESHOLD = 0.2
	NMS_THRESHOLD = 0.4
	COLORS = [(0, 255, 255), (255, 255, 0), (0, 255, 0), (255, 0, 0)]

	class_names = []
	with open("classes.txt", "r") as f:
	class_names = [cname.strip() for cname in f.readlines()]

	def f1_loss(y_true:torch.Tensor, y_pred:torch.Tensor, is_training=False) -> torch.Tensor:
	'''Calculate F1 score. Can work with gpu tensors

	The original implmentation is written by Michal Haltuf on Kaggle.

	Returns
	-------
	torch.Tensor
	`ndim` == 1. 0 <= val <= 1

	/*
	This requires adding the "include" directory of your Python installation to the include diretories
	of your project, e.g., in Visual Studio you'd add `C:\Program Files\Python36\include`.
	You also need to add the 'include' directory of your NumPy package, e.g.
	`C:\Program Files\PythonXX\Lib\site-packages\numpy\core\include`.

	Additionally, you need to link your "python3#.lib" library, e.g. `C:\Program Files\Python3X\libs\python3X.lib`.
	*/

	// python bindings