Amritpal Singh

The SikhNet "Gurbani for e-Book Readers" page provides downloadable spiritual literature optimized for e-ink reading devices to reduce eye strain. Users can access a wide array of resources, including Nitnem Banis, multi-volume sets, and complete PDFs of the Sri Guru Granth Sahib in both Gurmukhi and English translation. Practical instructions are included to help users easily transfer and manage these files via a standard USB connection. https://sikhnet.com/pages/gurbani-e-book-readers

Anaconda Distribution is an all-in-one, open-source Python platform designed to simplify package management, environment setup, and deployment for data science and machine learning. 1. Pre-packaged Data Science Libraries It comes pre-installed with over 1,500 essential data science and machine learning packages like NumPy, Pandas, and Scikit-learn right out of the box. 2. Effortless Environment Management Anaconda makes creating, isolating, and switching between different Python environments seamless, preventing dependency conflicts across your various projects. 3. Cross-Platform Compatibility It works identically across Windows, macOS, and Linux, ensuring your code and development setup remain consistent regardless of the operating system you use. 4. Simplified Package Installation The included conda package manager effortlessly handles complex C/C++ library dependencies that traditional Python tools like pip often struggle to install. 5. GUI and Tool Integration It includes Anaconda ...

I did some research, and concluded that it is still worth learning in today's day and age when AI can generate just about any kind of code snippets. But only experienced programmers can judge AI's output to fullest. I am going to get my Python skills sharpened now. I am going to take Udemy's course on Python for Data Science and Machine Learning Bootcamp .

This snippet computes C-accelerated Dynamic Time Warping using dtaidistance on identical sequences. Setting window=1 limits temporal drift off the main diagonal, while use_pruning=False turns off C-level early-stopping heuristics—guaranteeing exact distance values instead of inf while tracking the strict 1 -to- 1  warping path. Source import numpy as np from dtaidistance import dtw # Sample 1D sequences sequence_a = np . array ([ 0.0 , 1.0 , 1.2 , 1.5 , 2.2 , 10.1 , 1.1 , 1.1 , 1.1 , 1.1 , 1.1 , 1.1 ], dtype = np . float64 ) sequence_b = np . array ([ 0.0 , 1.0 , 1.2 , 1.5 , 2.2 , 10.1 , 1.1 , 1.1 , 1.1 , 1.1 , 1.1 , 1.1 ], dtype = np . float64 ) WINDOW_SIZE = 1 # Disable C-level pruning heuristics explicitly distance = dtw . distance ( sequence_a , sequence_b , window = WINDOW_SIZE , use_pruning = False # Forces full computation without inf cutoff ) path = dtw . warping_path ( sequence_a , ...

 All right, I just learned that there is something called algorithmic pricing. I think it will be good research a little on this topic.

I am going to take this this Udemy course https://www.udemy.com/course/time-series-classification-in-python/  on Time Series Data Analysis. I am mainly curious about different open source algorithms available for time series pattern recognition. I have background in Computer Science/Engineering, so I am hoping I can easily digest it.