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Transformers in Action, Video Edition

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Transformers in Action, Video Edition (Size: 1.8 GB)
  Get Bonus Downloads Here.url 204.8 B
  ~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 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 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 KB
  013. Chapter 3 Encoder-only models.mp4 27 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 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 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 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 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 KB
  045. Chapter 9 GPU-level optimization Tiling, threads, and memory.mp4 48 MB
  046. Chapter 9 Extending long-context windows.en.srt 14.1 KB
  046. Chapter 9 Extending long-context windows.mp4 49 MB
  047. Chapter 9 Summary.en.srt 1.4 KB
  047. Chapter 9 Summary.mp4 7.5 MB
  048. Chapter 10 Ethical and responsible large language models.en.srt 7.9 KB
  048. Chapter 10 Ethical and responsible large language models.mp4 30 MB
  049. Chapter 10 Transparency and explainability of LLMs.en.srt 13.3 KB
  049. Chapter 10 Transparency and explainability of LLMs.mp4 39.7 MB
  050. Chapter 10 Responsible use of LLMs.en.srt 6.3 KB
  050. Chapter 10 Responsible use of LLMs.mp4 18.2 MB
  051. Chapter 10 Safeguarding your language model.en.srt 20.5 KB
  051. Chapter 10 Safeguarding your language model.mp4 57.8 MB
  052. Chapter 10 Summary.en.srt 1.6 KB
  052. Chapter 10 Summary.mp4 10 MB
  Bonus Resources.txt 102.4 B

Description


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.

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