Software Engineering is the discipline of applying engineering principles and practices to the creation, maintenance, and design of software for a variety of applications. This topic covers a broad range of areas including requirements analysis, software design, programming, project management, testing, and maintenance. It emphasizes systematic, disciplined, and quantifiable approaches to the successful development of high-quality, reliable software systems.
A knowledge base bridging theorical with real-world applications.
A curated collection of hands-on notebooks exploring core machine and deep learning concepts. Each notebook focuses on a specific topic - from linear models and foundational elements like broadcasting and autograd to advanced tasks such as custom layers, transfer learning, sequence modeling with RNNs, and representation learning with autoencoders.
Demos and sample code on ML for engineers
The Code-Sleuth's Notebook
Amazon USA Financial Data : Insights across all states, focusing on R&D, marketing, campaigns, and profit. Features log normalization to stabilize skewed data and aligns with Gaussian distribution for better analysis. Free Excel sheet available for practice.
Runs command-line pipelines embedded in Markdown and CommonMark documents. Keeps your authored docs up to date. Even usable as an alternative to IPython notebooks.
This GitHub repository offers a dual-faceted resource for Python enthusiasts. It features 'practice_python' Jupyter notebooks for interactive learning, spanning basics to advanced topics, and a Flask project that illustrates real-world Python use, including user management and location tracking.
ByteCore Byte is a simplified version of the ByteCore CPU showcased in a Jupyter notebook, using 1-byte addressing to operate within a 256-byte memory limit. This repository features demonstrations of ByteCore Assembly through both basic and advanced programming examples.
Created by Software engineers