🌌 Omniverse: A Journey Through Knowledge

Welcome to the Omniverse, a vast expanse of knowledge spanning various domains of science, technology, and mathematics. Let’s embark on this cosmic journey!

Notations

• Machine Learning Notations

Influential Ideas and Papers

• Generative Pre-trained Transformers
  • Notations
  • The Concept of Generative Pre-trained Transformers (GPT)
  • The Implementation of Generative Pre-trained Transformers (GPT)
  • Training a Mini-GPT to Learn Two-Digit Addition

Playbook

• How to Calculate the Number of FLOPs in GPT-2
• How to Inspect Function and Class Signatures in Python
• Why Does Cosine Annealing With Warmup Stabilize Training?
• Softmax Preserves Order, Is Translation Invariant But Not Invariant Under Scaling

Deep Learning

• Training Chronicles
  • The Loss Landscape

Software Engineering

• Continuous Integration (CI) Workflow
• Dependency Inversion Principle
• Concurrency, Parallelism and Asynchronous Programming
  • A Rudimentary Introduction to Generator and Yield in Python
• RESTful API
  • Application: Designing a RESTful Banking API with FastAPI and SQLAlchemy

Computer Science

• Type Theory, A Very Rudimentary Introduction
  • Subtypes
  • Type Safety
  • Subsumption
  • Generics and Type Variables
  • Bound and Constraint in Generics and Type Variables
  • Invariance, Covariance and Contravariance
  • Function Overloading
  • Sentinel Types

Data Structures and Algorithms

• Complexity Analysis
  • Master Theorem
• Stack
  • Concept
• Linear Search
  • Concept
• Binary Search
  • Concept
  • Koko Eating Bananas

Linear Algebra

• Preliminaries
  • Fields
  • Systems of Linear Equations
• Vectors
  • Vector and Its Definition
  • Vector and Its Operations
  • Vector Norm and Distance
  • A First Look at Vector Products

Operations

• The Lifecycle of an AIOps System
  • Stage 1. Problem Formulation
  • Stage 2. Project Scoping And Framing The Problem
  • Stage 3. Data Pipeline (Data Engineering and DataOps)
  • Stage 4. Data Extraction (MLOps), Data Analysis (Data Science), Data Preparation (Data Science)
  • Stage 5. Model Development and Training (MLOps)
• Distributed Systems
  • Notations
  • Basics Of Distributed Data Parallelism

References, Resources and Roadmap

• Bibliography
• IEEE (Style) Citations

API Reference

• API Reference

🤖 Machine Learning

Machine Learning (ML) is a subset of artificial intelligence (AI) where computers are trained to learn from data. Instead of being explicitly programmed to perform a task, an ML model uses algorithms and statistical techniques to understand and make predictions or decisions based on data. Dive in to explore algorithms, techniques, and real-world applications that are transforming industries and our daily lives.

🧠 Deep Learning

Deep Learning is a subfield of machine learning that focuses on algorithms inspired by the structure and function of the brain called artificial neural networks. These networks can learn and make independent decisions by processing data on their own. From image and voice recognition to generating art and music, deep learning is at the forefront of pushing the boundaries of what machines can do.

📊 Data Science

Data Science is the art and science of extracting meaningful insights from raw data. It combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract valuable information from data. In this modern age where data is the new oil, data science plays a pivotal role in making informed decisions and predictions in various sectors.

🎲 Probability

Probability is the mathematical study of random events. It provides the theoretical foundation for statistics, which in turn is essential for fields like data science and machine learning. Delve (thanks ChatGPT) into the world of chance, randomness, and uncertainty, and understand how we can make sense of the unpredictable.

➗ Mathematics

Mathematics is the abstract science of number, quantity, and space, either as abstract concepts (pure mathematics) or as applied to other disciplines such as physics and engineering (applied mathematics). Journey through the realms of calculus, algebra, geometry, and more, as we explore the language of the universe.

💻 Software Engineering

Software Engineering is the systematic application of engineering approaches to the development of software. It encompasses everything from system architecture and design to coding, testing, and maintenance. Discover the principles, best practices, and cutting-edge technologies that power the digital world we live in.