Udemy - LLM Quantization and Compression - Theoretical Core

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size3 GB
  • Uploaded Byfreecoursewb
  • Downloads6
  • Last checkedJul. 10th '26
  • Date uploadedJul. 10th '26
  • Seeders 0
  • Leechers0

Infohash : C9419478E9251A890A01A98FA28518DB59E6C9E3

LLM Quantization and Compression: Theoretical Core

https://WebToolTip.com

Published 6/2026
Created by Bhushan S
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 49 Lectures ( 4h 2m ) | Size: 3.1 GB

Study how multi-billion parameter networks are compressed into low-precision representations for resource-constr...

What you'll learn
⚡ Master the core principles of Post-Training Quantization (PTQ).
⚡ Deconstruct the architecture and tradeoffs of Activation-aware Weight Quantization (AWQ).
⚡ Analyze the design patterns governing Low-Rank Adaptation (LoRA).
⚡ Build a deep mental model of Pruning Theory at scale.

Requirements
❗ No coding experience is required. We focus entirely on system design and core theoretical concepts.
❗ A basic interest in technology systems, algorithms, or computer science architecture.
❗ No special software or local development environment setup is needed.

Files:

[ WebToolTip.com ] Udemy - LLM Quantization and Compression - Theoretical Core
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction
    • 1. Introduction.mp4 (28.7 MB)
    • 2. Strategic Governance of Post-Training Quantization (PTQ).mp4 (66.2 MB)
    • 3. Practical Anatomy of Activation-aware Weight Quantization (AWQ).mp4 (66.3 MB)
    • 4. Foundational Models for Low-Rank Adaptation (LoRA).mp4 (66.3 MB)
    • 5. Core Principles of Pruning Theory.mp4 (64.1 MB)
    • 6. Understanding Architectural Trade-offs.mp4 (49.6 MB)
    • 7. Exploring Design Anti-patterns.mp4 (61.8 MB)
    2 - Machine Learning Paradigms & Bias-Variance Bounds
    • 10. Advanced Concepts in Low-Rank Adaptation (LoRA).mp4 (66.0 MB)
    • 11. Evaluating Pruning Theory.mp4 (61.8 MB)
    • 12. Understanding Architectural Trade-offs.mp4 (49.3 MB)
    • 13. Exploring Design Anti-patterns.mp4 (59.3 MB)
    • 8. Analyzing Post-Training Quantization (PTQ).mp4 (69.0 MB)
    • 9. Deep Dive into Activation-aware Weight Quantization (AWQ).mp4 (67.7 MB)
    3 - Deep Neural Networks & Gradient Propagation Models
    • 14. Deconstructing Post-Training Quantization (PTQ).mp4 (66.5 MB)
    • 15. Analyzing Activation-aware Weight Quantization (AWQ).mp4 (68.6 MB)
    • 16. Foundational Models for Low-Rank Adaptation (LoRA).mp4 (64.9 MB)
    • 17. Introduction to Pruning Theory.mp4 (63.8 MB)
    • 18. Advanced Concepts in Architectural Trade-offs.mp4 (62.9 MB)
    • 19. Core Principles of Design Anti-patterns.mp4 (63.0 MB)
    4 - Natural Language Processing & Embedding Geometries
    • 20. Introduction to Post-Training Quantization (PTQ).mp4 (64.8 MB)
    • 21. Advanced Concepts in Activation-aware Weight Quantization (AWQ).mp4 (68.2 MB)
    • 22. Core Principles of Low-Rank Adaptation (LoRA).mp4 (63.9 MB)
    • 23. Evaluating Pruning Theory.mp4 (60.3 MB)
    • 24. Exploring Architectural Trade-offs.mp4 (65.7 MB)
    • 25. Understanding Design Anti-patterns.mp4 (60.5 MB)
    5 - Transformer Architectures & Self-Attention Mechanics
    • 26. Evaluating Post-Training Quantization (PTQ).mp4 (70.2 MB)
    • 27. Exploring Activation-aware Weight Quantization (AWQ).mp4 (69.4 MB)
    • 28. Understanding Low-Rank Adaptation (LoRA).mp4 (65.7 MB)
    • 29. Practical Anatomy of Pruning Theory.mp4 (57.3 MB)
    • 30. Deconstructing Architectural Trade-offs.mp4 (66.0 MB)
    • 31. Analyzing Design Anti-patterns.mp4 (62.3 MB)
    6 - Reinforcement Learning & Markov Decision Steps
    • 32. Foundational Models for Post-Training Quantization (PTQ).mp4 (67.7 MB)
    • 33. Core Principles of Activation-aware Weight Quantization (AWQ).mp4 (68.0 MB)
    • 34. Evaluating Low-Rank Adaptation (LoRA).mp4 (65.6 MB)
    • 35. Exploring Pruning Theory.mp4 (62.8 MB)
    • 36. Understanding Architectural Trade-offs.mp4 (62.4 MB)
    • 37. Practical Anatomy of Design Anti-patterns.mp4 (59.7 MB)
    7 - Explainable AI, Model Auditing & Ethical Governance
    • 38. Practical Anatomy of Post-Training Quantization (PTQ).mp4 (66.0 MB)
    • 39. Deconstructing Activation-aware Weight Quantization (AWQ).mp4 (68.3 MB)
    • 40. Analyzing Low-Rank Adaptation (LoRA).mp4 (66.8 MB)
    • 41. Deep Dive into Pruning Theory.mp4 (61.4 MB)
    • 42. Advanced Concepts in Architectural Trade-offs.mp4 (63.5 MB)
    • 43. Core Principles of Design Anti-patterns.mp4 (62.7 MB)
    8 - Generative Models GANs & Latent Diffusion Systems
    • 44. Understanding Post-Training Quantization (PTQ).mp4 (66.2 MB)
    • 45. Core Principles of Activation-aware Weight Quantization (AWQ).mp4 (67.5 MB)
    • 46. Understanding Low-Rank Adaptation (LoRA).mp4 (65.5 MB)
    • 47. Practical Anatomy of Pruning Theory.mp4 (63.9 MB)
    • 48. Deconstructing Architectural Trade-offs.mp4 (62.7 MB)
    • 49. Analyzing Design Anti-patterns.mp4 (62.3 MB)
    • Bonus Resources.txt (0.1 KB)

Code:

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