Udemy - LLM Concepts Deep Dive - Conceptual Mastery for Developers

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size3.6 GB
  • Uploaded Byfreecoursewb
  • Downloads42
  • Last checkedMay. 09th '25
  • Date uploadedMay. 08th '25
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Infohash : 464EFD664C59DF26987C248B6A050C7425876A12

LLM Concepts Deep Dive: Conceptual Mastery for Developers

https://WebToolTip.com

Published 5/2025
Created by Koushik Kothagal
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 36 Lectures ( 2h 51m ) | Size: 3.6 GB

Master transformers, embeddings, and RAG. Learn how modern AI works and use vector databases for real-world solutions.

What you'll learn
Grasp the foundational concepts behind Large Language Models (LLMs), including what models are and the core language model tasks
Understand autoencoding, autoregression, and how LLMs perform text prediction and completion
Learn about pre-training, instruct tuning, and fine-tuning of AI models
Master the concepts of tokens and embeddings
Learn how how tokenization works, how token boundaries are formed, and how word frequencies are identified
Comprehend the importance of embeddings, how they represent text in N-dimensional space, and how to use them for text similarity tasks
Dive deep into transformer architecture, including how attention mechanisms work and why they are crucial for modern LLMs
Analyze the challenges of context length, context limits, and the stateless nature of LLMs, along with strategies to handle them effectively
Explore Retrieval-Augmented Generation (RAG) and learn how to implement advanced solutions using vector databases for practical AI applications
Build conceptual mastery that aligns with what top AI companies screen for in technical interviews

Requirements
Some familiarity working with an LLM like ChatGPT or Claude
No machine learning knowledge required
No advanced mathematics required

Files:

[ WebToolTip.com ] Udemy - LLM Concepts Deep Dive - Conceptual Mastery for Developers
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Language Modeling And Training
    • 1 -Understanding the Concept of Model.mp4 (114.2 MB)
    • 2 -Language Model Tasks and Auto Encoding.mp4 (79.0 MB)
    • 3 -Auto Regression and Text Prediction.mp4 (121.6 MB)
    • 4 -Text completion.mp4 (78.4 MB)
    • 5 -Audience Questions.mp4 (127.1 MB)
    2 - Training Methodologies
    • 1 -Pre-training.mp4 (144.6 MB)
    • 2 -Instruct tuning.mp4 (156.8 MB)
    • 3 -Fine Tuning.mp4 (88.1 MB)
    • 4 -Audience Questions.mp4 (72.3 MB)
    • 5 -Introduction to Fine-Tuning AI Models.mp4 (38.9 MB)
    3 - Tokens and Embeddings
    • 1 -Introduction to Tokens and Embeddings.mp4 (71.5 MB)
    • 10 -Audience Questions.mp4 (29.3 MB)
    • 2 -Tokenization Explained.mp4 (133.5 MB)
    • 3 -Visualizing Tokenization.mp4 (26.6 MB)
    • 4 -How token boundaries are formed.mp4 (220.3 MB)
    • 5 -How word frequencies are identified.mp4 (112.9 MB)
    • 6 -Embeddings and Their Importance.mp4 (314.9 MB)
    • 7 -Exploring Embeddings and the N-dimensional space.mp4 (116.6 MB)
    • 8 -Embedding Math. Mind-Blowing Examples.mp4 (58.0 MB)
    • 9 -Tokenization and Embeddings.mp4 (76.5 MB)
    4 - Knowledge Assessment and Semantic Similarity
    • 1 -Quiz Time Test Your Knowledge.mp4 (124.6 MB)
    • 2 -The Concept of Text Similarity.mp4 (106.2 MB)
    • 3 -Audience Questions.mp4 (71.1 MB)
    5 - Transformer Architecture
    • 1 -From Tokens To Text.mp4 (151.1 MB)
    • 2 -Introduction to Transformer Architecture.mp4 (109.7 MB)
    • 3 -Understanding Attention in LLMs.mp4 (137.8 MB)
    • 4 -Addressing Questions on Transformer Architecture.mp4 (101.2 MB)
    6 - Context Management
    • 1 -Introduction to Context Length in LLMs.mp4 (70.9 MB)
    • 2 -Challenges with Context Limit and Statelessness.mp4 (132.9 MB)
    7 - RAG and Vector Databases
    • 1 -Introducing Retrieval Augmented Generation (RAG).mp4 (124.0 MB)
    • 2 -Some important terminologies.mp4 (68.1 MB)
    • 3 -Vector Databases and Their Role in RAG.mp4 (88.6 MB)
    • 4 -How Vector Databases Work.mp4 (71.7 MB)
    • 5 -Vector Database Interaction Pseudocode.mp4 (32.6 MB)
    • 6 -The RAG Pipeline.mp4 (41.1 MB)
    • 7 -Q&A and Final Thoughts.mp4 (62.6 MB)
    • Bonus Resources.txt (0.1 KB)

Code:

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