
Building Your Own Large Language Model
Et Tu Code
Building Your Own Large Language Model: A Step-by-Step Guide is your comprehensive roadmap to crafting custom language models tailored to diverse applications.
From laying the groundwork in Language Model Development Fundamentals to navigating through Essential NLP Concepts and Framework Selection, each step is meticulously detailed to ensure mastery. You'll learn the art of Data Collection and Preprocessing, crucial for gathering and refining training data, followed by the craft of Scalable Model Architecture Design. Training and Fine-Tuning Strategies guide you through efficiently honing your model's capabilities, while Performance Evaluation and Validation ensure accuracy. Model Deployment integrates your creation seamlessly into real-world applications like chatbots or language translation tools, while Task-Specific Fine-Tuning customizes it for sentiment analysis or text summarization. Ethical Considerations address biases and ethical concerns, while Performance Optimization techniques enhance efficiency. Through Exploration of Large Language Models, you'll grasp the landscape's nuances, preparing for Application Integration and Scaling with Distributed Training. Continuous Improvement methodologies, coupled with a focus on Interpretability and a glimpse into Future Trends, equip you for ongoing success. Real-world Case Studies provide practical insights, and Community Engagement fosters collaboration.
Concluding with key takeaways and recommendations, this guide propels you into the dynamic realm of large language model development with confidence and clarity. 🚀
Duration - 5h 46m.
Author - Et Tu Code.
Narrator - Helen Green.
Published Date - Saturday, 20 January 2024.
Copyright - © 2023 Et Tu Code ©.
Location:
United States
Description:
Building Your Own Large Language Model: A Step-by-Step Guide is your comprehensive roadmap to crafting custom language models tailored to diverse applications. From laying the groundwork in Language Model Development Fundamentals to navigating through Essential NLP Concepts and Framework Selection, each step is meticulously detailed to ensure mastery. You'll learn the art of Data Collection and Preprocessing, crucial for gathering and refining training data, followed by the craft of Scalable Model Architecture Design. Training and Fine-Tuning Strategies guide you through efficiently honing your model's capabilities, while Performance Evaluation and Validation ensure accuracy. Model Deployment integrates your creation seamlessly into real-world applications like chatbots or language translation tools, while Task-Specific Fine-Tuning customizes it for sentiment analysis or text summarization. Ethical Considerations address biases and ethical concerns, while Performance Optimization techniques enhance efficiency. Through Exploration of Large Language Models, you'll grasp the landscape's nuances, preparing for Application Integration and Scaling with Distributed Training. Continuous Improvement methodologies, coupled with a focus on Interpretability and a glimpse into Future Trends, equip you for ongoing success. Real-world Case Studies provide practical insights, and Community Engagement fosters collaboration. Concluding with key takeaways and recommendations, this guide propels you into the dynamic realm of large language model development with confidence and clarity. 🚀 Duration - 5h 46m. Author - Et Tu Code. Narrator - Helen Green. Published Date - Saturday, 20 January 2024. Copyright - © 2023 Et Tu Code ©.
Language:
English
Opening Credits
Duración:02:13:28
2 preface
Duración:02:43:16
3 Introduction to language model development
Duración:05:48:16
4 Basics of natural language processing
Duración:03:20:48
5 Choosing the right framework
Duración:04:57:12
6 collecting and preprocessing data
Duración:04:43:16
7 model architecture design
Duración:05:26:04
8 training and fine tuning
Duración:05:52:28
9 evaluation metrics and validation
Duración:05:33:02
10 deploying your language model
Duración:04:35:00
11 fine tuning for specific use cases
Duración:06:44:55
12 handling ethical and bias considerations
Duración:04:27:09
13 optimizing performance and efficiency
Duración:04:50:55
14 popular large language models
Duración:06:06:38
15 popular large language models gpt 3 (generative pre trained transformer 3)
Duración:04:36:57
16 popular large language models bert (bidirectional encoder representations from transformers)
Duración:03:59:31
17 popular large language models t5 (text to text transfer transformer)
Duración:05:49:12
18 popular large language models xlnet
Duración:03:59:50
19 popular large language models roberta (robustly optimized bert approach)
Duración:05:15:31
20 popular large language models llama 2
Duración:04:21:00
21 popular large language models google's gemini
Duración:06:04:21
22 integrating language model with applications
Duración:04:38:02
23 scaling and distributed training
Duración:04:15:38
24 continuous improvement and maintenance
Duración:03:15:36
25 interpretable ai and explainability
Duración:06:20:24
26 challenges and future trends
Duración:04:24:21
27 case studies and project examples
Duración:04:50:57
28 community and collaboration
Duración:04:57:07
29 conclusion
Duración:04:47:33
30 glossary
Duración:04:36:31
31 bibliography
Duración:05:00:12
Ending Credits
Duración:01:14:38