Udemy - Mastering Vector Databases and Embedding Models in 2025

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size790.7 MB
  • Uploaded Byfreecoursewb
  • Downloads133
  • Last checkedJun. 21st '26
  • Date uploadedJun. 19th '26
  • Seeders 7
  • Leechers3

Infohash : D61F3DA245305390F7A50DBFE0060E3C5B818D2F

Mastering Vector Databases & Embedding Models in 2025

https://WebToolTip.com

Last updated 8/2025
Created by Tensor Teach
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 22 Lectures ( 1h 58m ) | Size: 790.8 MB

Learn embeddings, similarity search, HNSW, IVF, semantic search, RAG, and recommender systems with hands-on examples.

What you'll learn
⚡ Explain what embeddings are and how they enable similarity search.
⚡ Learn how to choose and fine-tune embedding models for custom applications.
⚡ Learn how vector databases work in terms of indexing & retrieval.
⚡ Familiarize yourself with the vector database landscape and different applications.

Requirements
❗ Basic Python knowledge recommended, but step-by-step coding lessons are provided.
❗ No prior experience with embeddings, vector databases, or similarity search is required.

Files:

[ WebToolTip.com ] Udemy - Mastering Vector Databases and Embedding Models in 2025
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Foundations of Embeddings & Similarity
    • 1. What Are Embeddings.en_US.srt (5.5 KB)
    • 1. What Are Embeddings.mp4 (12.2 MB)
    • 2. Section 1 Notebook.html (5.4 KB)
    • 2. Section1_Notebook.ipynb.bin (307.2 KB)
    • 3. Creating & Visualizing Embeddings with Sentence Transformers.en_US.srt (7.3 KB)
    • 3. Creating & Visualizing Embeddings with Sentence Transformers.mp4 (54.9 MB)
    • 4. Understanding Vector Similarity Metrics.en_US.srt (8.1 KB)
    • 4. Understanding Vector Similarity Metrics.mp4 (35.8 MB)
    • 5. Creating a Mini-Search Engine Using Embeddings.en_US.srt (3.4 KB)
    • 5. Creating a Mini-Search Engine Using Embeddings.mp4 (12.7 MB)
    2 - Choosing and Using Embedding Models
    • 10. Fine-Tuning Embedding Models with Contrastive Loss.en_US.srt (15.7 KB)
    • 10. Fine-Tuning Embedding Models with Contrastive Loss.mp4 (117.4 MB)
    • 10. Fine_Tuning_Embedding_Models.ipynb.bin (257.5 KB)
    • 6. Embedding_Models_Tokenizer_&_Architecture.ipynb.bin (33.0 KB)
    • 6. Understanding Embedding Models Tokenizer & Architecture.en_US.srt (16.0 KB)
    • 6. Understanding Embedding Models Tokenizer & Architecture.mp4 (102.3 MB)
    • 7. Evaluating and Selecting Text Embedding Models.en_US.srt (8.3 KB)
    • 7. Evaluating and Selecting Text Embedding Models.mp4 (59.0 MB)
    • 8. Multimodal Models - A Brief Overview.en_US.srt (5.0 KB)
    • 8. Multimodal Models - A Brief Overview.mp4 (13.5 MB)
    • 9. Multimodal_Embeddings.ipynb.bin (120.0 KB)
    • 9. Working with Multimodal Models in Transformers.en_US.srt (9.2 KB)
    • 9. Working with Multimodal Models in Transformers.mp4 (56.9 MB)
    3 - How Vector Databases Work
    • 11. Introduction to Vector Database Indexing & Retrieval Strategies.en_US.srt (6.8 KB)
    • 11. Introduction to Vector Database Indexing & Retrieval Strategies.mp4 (18.1 MB)
    • 12. HNSW - Indexing & Retrieval Explained.en_US.srt (8.6 KB)
    • 12. HNSW - Indexing & Retrieval Explained.mp4 (22.4 MB)
    • 13. FAISS_Implementations.ipynb.bin (162.6 KB)
    • 13. Section 3 Notebook.html (5.4 KB)
    • 14. HNSW Implementation in FAISS.en_US.srt (8.8 KB)
    • 14. HNSW Implementation in FAISS.mp4 (63.9 MB)
    • 15. IVF - Indexing & Retrieval Explained.en_US.srt (7.5 KB)
    • 15. IVF - Indexing & Retrieval Explained.mp4 (20.2 MB)
    • 16. IVF Implementation in FAISS.en_US.srt (3.4 KB)
    • 16. IVF Implementation in FAISS.mp4 (16.9 MB)
    4 - Vector Databases Landscape & Applications
    • 17. Overview of Vector Database Landscape + Pinecone Introduction.en_US.srt (7.1 KB)
    • 17. Overview of Vector Database Landscape + Pinecone Introduction.mp4 (20.3 MB)
    • 18. Section 4 Notebook.html (5.4 KB)
    • 18. VectorDB_Applications.ipynb.bin (204.9 KB)
    • 19. Semantic Search with Pinecone.en_US.srt (10.8 KB)
    • 19. Semantic Search with Pinecone.mp4 (61.4 MB)
    • 20. Retrieval Augmented Generation (RAG) with Pinecone.en_US.srt (7.7 KB)
    • 20. Retrieval Augmented Generation (RAG) with Pinecone.mp4 (25.6 MB)
    • 21. Recommender Systems with Pinecone.en_US.srt (6.3 KB)
    • 21. Recommender Systems with Pinecone.mp4 (56.1 MB)
    • 22. Bonus Lecture - Free AI Research Newsletter.en_US.srt (4.4 KB)
    • 22. Bonus Lecture - Free AI Research Newsletter.mp4 (19.9 MB)
    • Bonus Resources.txt (0.1 KB)

Code:

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