Hello and welcome to my personal notes. This is a series of notes that I maintain around a number of topics not necessarilly related with each other. These notes are... just notes i.e. do not necessarilly go in depth or covering a topic exhaustively. The main idea behind these notes is to give the gist of a topic and provide references for further more in depth research. The qubit in quantum computing is the analogus to the bit in the casual(?) computing. However, a qubit can have more than two states (i.e. on/off). Similarly, qubit-notes are tiny but sometimes not so tiny.
- qubit-note: Linear Regression Model
- qubit-note: K-means for Image Quantization
- qubit-note: Stochastic Grdaient Descent
- qubit-note: Gradient Descent
- qubit-note: Monte Carlo Integration
- qubit-note: Thompson Sampling
- qubit-note: Architecture Patterns-Service Oriented Architecture Pattern
- qubit-note: Data Replication
- qubit-note: Architecture Patterns-Pipeline Pattern
- qubit-note: Some Best Practices in API Design
- qubit-note: Semantic Caching
- qubit-note: Unique ID Generation
- qubit-note: Architecture Patterns-Ports and Adapters Pattern
- qubit-note: Architecture Patterns-Layers Pattern
- qubit-note: Sharded Counters
- qubit-note: Partitioning
- qubit-note: Digital Twins
- qubit-note: Circuit Breaker Pattern
- qubit-note: Consistency Models
- qubit-note: Backend-for-frontend Pattern
- qubit-note: Load Balancing
- qubit-note: Managing Distributed Workflows
- qubit-note: Domain Name System
- qubit-note: Document Fusion & Multi-stage Retrieval for Multi-modal RAG
- qubit-note: RAG or Fine Tuning?
- qubit-note: Evaluate a RAG-based System
- qubit-note: AI Agent vs MCP
- qubit-note: Visual Language Models
- qubit-note: Indexing for RAG
- qubit-note: Training Patterns for Distributed ML
- qubit-note: Dimensionality Reduction with PCA
- qubit-note: Data Ingestion Patterns for Distributed ML
- qubit-note: Tactics to Increase LLM Reliability
- qubit-note: ML Model Compression
- quibit-note: Retrieval Augmented Generation (RAG)
- qubit-note: Detecting Data Drift
- qubit-note: Detecting Concept Drift
- qubit-note: Prompt Methods
- qubit-note: 13 + 1 Steps For a Successful ML Project
- qubit-note: Bias-Variance Dilemma
- qubit-note: Collection of Training Data
- qubit-note: Model Context Protocol
- qubit-note: Hyperparameter Tuning in ML Models
- qubit-note: Apache Spark Series 4-Overview of RDDs
- qubit-note: Coroutine Chaining & Asynchronous Queues in Python
- qubit-note: Apache Spark Series 3-Create a Toy Apache Spark Cluster With Docker
- qubit-note: Apache Spark Series 2-Hello World
- qubit-note: Apache Spark Series 1-Application concepts
- qubit-note: Build a Local MCP Server & Client
- qubit-note: Handle Dynamic Pages with ExpressJS
- qubit-note: Serving Images With ExpressJS
- qubit-node: Hello ExpressJS
- qubit-note: Pub/Sub Model in Redis
- qubit-note: Callback Chaining in NodeJS
- qubit-note: Serving ML Models With FastAPI & Ray
- qubit-note: Task-based Parallelism With Ray Part 1
- qubit-note: Deploy Microservices with Kubernetes 101
- qubit-note: Use Django with Apache
- qubit-note: Django with Docker
- qubit-note: P2P Communication Modes with MPI
- qubit-note: Point-to-Point Communication with MPI
- qubit-note: Object Communication with MPI
- qubit-note: MPI Hello World