Transformers in Action, Video Edition

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size1.8 GB
  • Uploaded Byfreecoursewb
  • Downloads160
  • Last checkedMay. 02nd '26
  • Date uploadedMay. 01st '26
  • Seeders 20
  • Leechers7

Infohash : 82ECE093DD0DEC4B67A3DF8FF91808C00CE74633

Transformers in Action, Video Edition

https://WebToolTip.com

Published 11/2025
By Nicole Koenigstein
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 6h 37m | Size: 1.75 GB

Understand the architecture that underpins today’s most powerful AI models.

Transformers are the superpower behind large language models (LLMs) like ChatGPT, Gemini, and Claude. Transformers in Action gives you the insights, practical techniques, and extensive code samples you need to adapt pretrained transformer models to new and exciting tasks.
Inside Transformers in Action you’ll learn
• How transformers and LLMs work
• Modeling families and architecture variants
• Efficient and specialized large language models
• Adapt HuggingFace models to new tasks
• Automate hyperparameter search with Ray Tune and Optuna
• Optimize LLM model performance
• Advanced prompting and zero/few-shot learning
• Text generation with reinforcement learning
• Responsible LLMs
Transformers in Action takes you from the origins of transformers all the way to fine-tuning an LLM for your own projects. Author

Nicole Koenigstein
nstrates the vital mathematical and theoretical background of the transformer architecture practically through executable Jupyter notebooks. You’ll discover advice on prompt engineering, as well as proven-and-tested methods for optimizing and tuning large language models. Plus, you’ll find unique coverage of AI ethics, specialized smaller models, and the decoder encoder architecture.

Files:

[ WebToolTip.com ] Transformers in Action, Video Edition
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here !
    • 001. Part 1 Foundations of modern transformer models.en.srt (2.5 KB)
    • 001. Part 1 Foundations of modern transformer models.mp4 (9.5 MB)
    • 002. Chapter 1 The need for transformers.en.srt (16.4 KB)
    • 002. Chapter 1 The need for transformers.mp4 (54.4 MB)
    • 003. Chapter 1 How to use transformers.en.srt (3.4 KB)
    • 003. Chapter 1 How to use transformers.mp4 (9.6 MB)
    • 004. Chapter 1 When and why to use transformers.en.srt (6.0 KB)
    • 004. Chapter 1 When and why to use transformers.mp4 (16.2 MB)
    • 005. Chapter 1 From transformer to LLM The lasting blueprint.en.srt (3.1 KB)
    • 005. Chapter 1 From transformer to LLM The lasting blueprint.mp4 (14.7 MB)
    • 006. Chapter 1 Summary.en.srt (2.1 KB)
    • 006. Chapter 1 Summary.mp4 (10.8 MB)
    • 007. Chapter 2 A deeper look into transformers.en.srt (14.7 KB)
    • 007. Chapter 2 A deeper look into transformers.mp4 (51.7 MB)
    • 008. Chapter 2 Model architecture.en.srt (46.2 KB)
    • 008. Chapter 2 Model architecture.mp4 (122.3 MB)
    • 009. Chapter 2 Summary.en.srt (1.8 KB)
    • 009. Chapter 2 Summary.mp4 (7.9 MB)
    • 010. Part 2 Generative transformers.en.srt (2.8 KB)
    • 010. Part 2 Generative transformers.mp4 (7.4 MB)
    • 011. Chapter 3 Model families and architecture variants.en.srt (4.0 KB)
    • 011. Chapter 3 Model families and architecture variants.mp4 (14.7 MB)
    • 012. Chapter 3 The decoder-only architecture.en.srt (8.6 KB)
    • 012. Chapter 3 The decoder-only architecture.mp4 (32.3 MB)
    • 013. Chapter 3 Encoder-only models.en.srt (7.0 KB)
    • 013. Chapter 3 Encoder-only models.mp4 (27.0 MB)
    • 014. Chapter 3 Embedding models and RAG.en.srt (16.4 KB)
    • 014. Chapter 3 Embedding models and RAG.mp4 (50.4 MB)
    • 015. Chapter 3 MoE in LLMs.en.srt (13.3 KB)
    • 015. Chapter 3 MoE in LLMs.mp4 (35.6 MB)
    • 016. Chapter 3 Summary.en.srt (1.5 KB)
    • 016. Chapter 3 Summary.mp4 (9.1 MB)
    • 017. Chapter 4 Text generation strategies and prompting techniques.en.srt (37.5 KB)
    • 017. Chapter 4 Text generation strategies and prompting techniques.mp4 (119.2 MB)
    • 018. Chapter 4 The art of prompting.en.srt (28.4 KB)
    • 018. Chapter 4 The art of prompting.mp4 (85.2 MB)
    • 019. Chapter 4 Summary.en.srt (1.8 KB)
    • 019. Chapter 4 Summary.mp4 (10.6 MB)
    • 020. Chapter 5 Preference alignment and retrieval-augmented generation.en.srt (15.4 KB)
    • 020. Chapter 5 Preference alignment and retrieval-augmented generation.mp4 (53.2 MB)
    • 021. Chapter 5 Aligning LLMs with direct preference optimization.en.srt (20.4 KB)
    • 021. Chapter 5 Aligning LLMs with direct preference optimization.mp4 (60.2 MB)
    • 022. Chapter 5 MixEval A benchmark for robust and cost-efficient evaluation.en.srt (5.1 KB)
    • 022. Chapter 5 MixEval A benchmark for robust and cost-efficient evaluation.mp4 (18.0 MB)
    • 023. Chapter 5 Retrieval-augmented generation.en.srt (19.4 KB)
    • 023. Chapter 5 Retrieval-augmented generation.mp4 (54.1 MB)
    • 024. Chapter 5 Summary.en.srt (1.2 KB)
    • 024. Chapter 5 Summary.mp4 (7.1 MB)
    • 025. Part 3 Specialized models.en.srt (2.9 KB)
    • 025. Part 3 Specialized models.mp4 (9.6 MB)
    • 026. Chapter 6 Multimodal models.en.srt (4.6 KB)
    • 026. Chapter 6 Multimodal models.mp4 (13.6 MB)
    • 027. Chapter 6 Combining modalities from different domains.en.srt (5.2 KB)
    • 027. Chapter 6 Combining modalities from different domains.mp4 (14.1 MB)
    • 028. Chapter 6 Modality-specific tokenization.en.srt (28.3 KB)
    • 028. Chapter 6 Modality-specific tokenization.mp4 (82.7 MB)
    • 029. Chapter 6 Multimodal RAG From PDF to images, tables, and cross-model comparison.en.srt (6.9 KB)
    • 029. Chapter 6 Multimodal RAG From PDF to images, tables, and cross-model comparison.mp4 (29.1 MB)
    • 030. Chapter 6 Summary.en.srt (1.1 KB)
    • 030. Chapter 6 Summary.mp4 (8.6 MB)
    • 031. Chapter 7 Efficient and specialized small language models.en.srt (7.2 KB)
    • 031. Chapter 7 Efficient and specialized small language models.mp4 (25.6 MB)
    • 032. Chapter 7 Small models as agents in a system of specialists.en.srt (5.0 KB)
    • 032. Chapter 7 Small models as agents in a system of specialists.mp4 (15.8 MB)
    • 033. Chapter 7 Classification with SLMs.en.srt (15.2 KB)
    • 033. Chapter 7 Classification with SLMs.mp4 (34.6 MB)
    • 034. Chapter 7 Adapting Gemma 3 270M for empathy and prosocial tone.en.srt (12.7 KB)
    • 034. Chapter 7 Adapting Gemma 3 270M for empathy and prosocial tone.mp4 (37.1 MB)
    • 035. Chapter 7 Adapting Gemma 3 270M for English_Spanish translation.en.srt (6.1 KB)
    • 035. Chapter 7 Adapting Gemma 3 270M for English_Spanish translation.mp4 (17.4 MB)
    • 036. Chapter 7 Broader use cases and complementary models.en.srt (6.7 KB)
    • 036. Chapter 7 Broader use cases and complementary models.mp4 (22.6 MB)
    • 037. Chapter 7 Summary.en.srt (3.1 KB)
    • 037. Chapter 7 Summary.mp4 (17.3 MB)
    • 038. Chapter 8 Training and evaluating large language models.en.srt (10.0 KB)
    • 038. Chapter 8 Training and evaluating large language models.mp4 (35.3 MB)
    • 039. Chapter 8 Model tuning and hyperparameter optimization.en.srt (14.9 KB)
    • 039. Chapter 8 Model tuning and hyperparameter optimization.mp4 (40.5 MB)
    • 040. Chapter 8 Parameter-efficient fine-tuning LLMs.en.srt (38.1 KB)
    • 040. Chapter 8 Parameter-efficient fine-tuning LLMs.mp4 (134.8 MB)
    • 041. Chapter 8 Summary.en.srt (1.6 KB)
    • 041. Chapter 8 Summary.mp4 (10.0 MB)
    • 042. Chapter 9 Optimizing and scaling large language models.en.srt (14.2 KB)
    • 042. Chapter 9 Optimizing and scaling large language models.mp4 (47.2 MB)
    • 043. Chapter 9 Sharding for memory optimization.en.srt (7.8 KB)
    • 043. Chapter 9 Sharding for memory optimization.mp4 (24.6 MB)
    • 044. Chapter 9 Inference optimization.en.srt (9.1 KB)
    • 044. Chapter 9 Inference optimization.mp4 (30.8 MB)
    • 045. Chapter 9 GPU-level optimization Tiling, threads, and memory.en.srt (15.0 KB)
    • 045. Chapter 9 GPU-level optimization Tiling, threads, and memory.mp4 (48.0 MB)
    • 046. Chapter 9 Extending long-context windows.en.srt (14.1 KB)
    • 046. Chapter 9 Extending long-context windows.mp4 (49.0 MB)
    • 047. Chapter 9 Summary.en.srt (1.4 KB)
    • 047. Chapter 9 Summary.mp4 (7.5 MB)
    • 048. Chapter 10 Ethical and responsible larg

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