SUMMARY.md

Table of contents

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• 🚀 About

👩‍💻 👩Freesoftware

• 🍉 The GNU Hurd
  • 😄 The files extension
  • 📽️ Tutorial for starting
  • 🚚 Continue Working for the Hurd
  • 🚴‍♂️ cgo
    • 👯‍♀️ Statically VS Dynamically binding
    • 🧌 Different ways in binding
    • 👨‍💻 Segfault
  • 🛃 Rust FFI
• 🧚🏻‍♂️ Programming
  • 📖 Introduction to programming
  • 📖 Mutable Value Semantics
  • 📖 Linked List
  • 📖 Rust
    • 📖 Keyword dyn
    • 📖 Tonic framework
    • 📖 Tokio
    • 📖 Rust read files
• 🎹 Algorithm
  • Two-pointer Technique
  • Linear Search Technique
  • Binary Search Technique
  • Counter Technique
  • In Place Technique
  • Prefix Sum Technique
  • N D Array
  • Sliding Window Technique
  • Juggling Technique

🛤️ AI techniques

• ⛓️ Chain
  • 🤟 LangChain
    • 🏈 Concepts
    • 🎾 Models
    • 🏉 Prompts
    • 🏈 Memory
    • 🥊 Indexes
• 🗄️ framework
  • 🧷 pytorch
  • 📓 Time components
  • 📓 burn
• 🍡 Adaptation
  • 🎁 LoRA
    • ℹ️ Matrix Factorization
    • 📀 SVD
      • ✝️ Distillation of SVD
      • 🦎 Eigenvalues of a covariance matrix
        • 🧧 Eigenvalues
        • 🏪 Covariance Matrix
    • 🛫 Checkpoint
  • 🎨 PEFT
• 🙋‍♂️ Training
  • 🛻 Training with QLoRA
  • 🦌 Deep Speed
• 🧠 Stable Diffusion
  • 🤑 Stable Diffusion model
  • 📼 Stable Diffusion v1 vs v2
  • 🤼‍♀️ The important parameters for stunning AI image
  • ⚾ Diffusion in image
  • 🚬 Classifier Free Guidance
  • ⚜️ Denoising strength
  • 👷 Stable Diffusion workflow
  • 📙 LoRA(Stable Diffusion)
  • 🗺️ Depth maps
  • 📋 CLIP
  • ⚕️ Embeddings
  • 🕠 VAE
  • 💥 Conditioning
  • 🍁 Diffusion sampling/samplers
  • 🥠 Prompt
  • 😄 ControlNet
    • 🪡 Settings Explained
    • 🐳 ControlNet with models
• 🦙 Large Language Model
  • ☺️ SMID
  • 👨‍🌾 ARM NEON
  • 🍊 Metal
  • 🏁 BLAS
  • 🍉 ggml
  • 💻 llama.cpp
  • 🎞️ Measuring model quality
  • 🥞 Type for NNC
  • 🥞 Token
  • 🤼‍♂️ Doc Retrieval && QA with LLMs
  • Hallucination(AI)
• 🐹 diffusers
  • 💪 Deconstruct the Stable Diffusion pipeline

🎹 Implementing

• 👨‍💻 diffusers
  • 📖 The Annotated Diffusion Model

🧩 Trending

• 📖 Trending
  • 📖 Vector database
  • 🍎 Programming Languages
    • 📖 Go & Rust manage their memories
    • 📖 Performance of Rust and Python
    • 📖 Rust ownership and borrowing
  • 📖 Neural Network
    • 🎹 Sliding window/convolutional filter
  • Quantum Machine Learning

🎾 Courses Collection

• 📖 Courses Collection
  • 📚 Academic In IT
    • 📝 Reflective Writing
  • 📖 UCB
    • 📖 CS 61A
      • 📖 Computer Science
      • 📖 Scheme
      • 📖 Python
      • 📖 Data Abstraction
      • 📖 Object-Oriented Programming
      • 📖 Interpreters
      • 📖 Streams
  • 🍎 MIT Algorithm Courses
    • 0️ MIT 18.01
      • 0️ Limits and continuity
      • 1️ Derivatives
      • 3️ Integrals
    • 1️ MIT 6.042J
      • 🔢 Number Theory
      • 📊 Graph Theory
        • 🌴 Graph and Trees
        • 🌲 Shortest Paths and Minimum Spanning Trees
    • 2️ MIT 6.006
      • Intro and asymptotic notation
      • Sorting and Trees
        • Sorting
        • Trees
      • Hashing
      • Graphs
      • Shortest Paths
      • Dynamic Programming
      • Advanced
    • 3️ MIT 6.046J
      • Divide and conquer
      • Dynamic programming
      • Greedy algorithms
      • Graph algorithms