Building, Training and Hardware for LLM AI
Et Tu Code
Building, Training, and Hardware for LLM AI is your comprehensive guide to mastering the development, training, and hardware infrastructure essential for Large Language Model (LLM) projects. With a focus on practical insights and step-by-step instructions, this eBook equips you with the knowledge to navigate the complexities of LLM development and deployment effectively.
Starting with an introduction to Language Model Development and the Basics of Natural Language Processing (NLP), you'll gain a solid foundation before delving into the critical decision-making process of Choosing the Right Framework and Architecture. Learn how to Collect and Preprocess Data effectively, ensuring your model's accuracy and efficiency from the outset.
Model Architecture Design and Evaluation Metrics are explored in detail, providing you with the tools to create robust models and validate their performance accurately. Throughout the journey, you'll also address ethical considerations and bias, optimizing performance and efficiency while ensuring fair and responsible AI deployment.
Explore the landscape of Popular Large Language Models, integrating them with applications seamlessly and continuously improving their functionality and interpretability. Real-world Case Studies and Project Examples offer invaluable insights into overcoming challenges and leveraging LLMs for various use cases.
The book doesn't stop at software; it provides an in-depth exploration of Hardware for LLM AI. From understanding the components to optimizing hardware for maximum efficiency, you'll learn how to create on-premises or cloud infrastructure tailored to your LLM needs.
Whether you're a seasoned developer or a newcomer to the field, "Building, Training, and Hardware for LLM AI" empowers you to navigate the complexities of LLM development with confidence, setting you on the path to success in the exciting world of large language models.
Duration - 18h 9m.
Author - Et Tu Code.
Narrator - Helen Green.
Published Date - Monday, 29 January 2024.
Copyright - © 2024 Et Tu Code ©.
Location:
United States
Description:
Building, Training, and Hardware for LLM AI is your comprehensive guide to mastering the development, training, and hardware infrastructure essential for Large Language Model (LLM) projects. With a focus on practical insights and step-by-step instructions, this eBook equips you with the knowledge to navigate the complexities of LLM development and deployment effectively. Starting with an introduction to Language Model Development and the Basics of Natural Language Processing (NLP), you'll gain a solid foundation before delving into the critical decision-making process of Choosing the Right Framework and Architecture. Learn how to Collect and Preprocess Data effectively, ensuring your model's accuracy and efficiency from the outset. Model Architecture Design and Evaluation Metrics are explored in detail, providing you with the tools to create robust models and validate their performance accurately. Throughout the journey, you'll also address ethical considerations and bias, optimizing performance and efficiency while ensuring fair and responsible AI deployment. Explore the landscape of Popular Large Language Models, integrating them with applications seamlessly and continuously improving their functionality and interpretability. Real-world Case Studies and Project Examples offer invaluable insights into overcoming challenges and leveraging LLMs for various use cases. The book doesn't stop at software; it provides an in-depth exploration of Hardware for LLM AI. From understanding the components to optimizing hardware for maximum efficiency, you'll learn how to create on-premises or cloud infrastructure tailored to your LLM needs. Whether you're a seasoned developer or a newcomer to the field, "Building, Training, and Hardware for LLM AI" empowers you to navigate the complexities of LLM development with confidence, setting you on the path to success in the exciting world of large language models. Duration - 18h 9m. Author - Et Tu Code. Narrator - Helen Green. Published Date - Monday, 29 January 2024. Copyright - © 2024 Et Tu Code ©.
Language:
English
Opening Credits
Duración:02:04:19
Preface
Duración:06:15:16
Part 1 building your own large language model
Duración:00:16:07
Introduction to language model development
Duración:05:54:04
Basics of natural language processing
Duración:03:26:38
Choosing the right framework
Duración:05:04:02
Collecting and preprocessing data
Duración:04:50:28
Model architecture design
Duración:05:29:04
Evaluation metrics and validation
Duración:05:11:50
Deploying your language model
Duración:04:42:21
Handling ethical and bias considerations
Duración:04:33:50
Optimizing performance and efficiency
Duración:04:56:24
Popular large language models
Duración:06:02:52
Popular large language models gpt 3 (generative pre trained transformer 3)
Duración:04:41:26
Popular large language models bert (bidirectional encoder representations from transformers)
Duración:04:03:16
Popular large language models t5 (text to text transfer transformer)
Duración:05:05:31
Popular large language models xlnet
Duración:04:05:38
Popular large language models roberta (robustly optimized bert approach)
Duración:05:21:14
Popular large language models llama 2
Duración:04:28:00
Popular large language models google's gemini
Duración:05:24:33
Integrating language model with applications
Duración:04:44:43
Continuous improvement and maintenance
Duración:03:21:04
Interpretable ai and explainability
Duración:06:26:33
Challenges and future trends
Duración:04:30:31
Case studies and project examples
Duración:04:56:07
Community and collaboration
Duración:04:21:19
Conclusion
Duración:04:55:38
Basics of natural language processing (nlp)
Duración:04:44:28
Choosing the right architecture
Duración:05:17:43
Data collection and preprocessing
Duración:05:20:28
Hyperparameter tuning
Duración:05:21:31
Transfer learning strategies
Duración:05:04:04
Addressing overfitting and regularization
Duración:05:13:40
Fine tuning for specific tasks
Duración:05:31:19
Steps on training large language models (llms)
Duración:03:20:50
Steps on training large language models (llms) step 1: define your objective
Duración:03:50:57
Steps on training large language models (llms) step 2: data collection and preparation
Duración:04:29:00
Steps on training large language models (llms) step 3: choose a pre trained model or architecture
Duración:03:44:31
Steps on training large language models (llms) step 4: model configuration
Duración:04:24:19
Steps on training large language models (llms) step 5: training process
Duración:02:54:26
Steps on training large language models (llms) step 6: model evaluation
Duración:04:44:45
Steps on training large language models (llms) step 7: hyperparameter tuning
Duración:05:50:38
Steps on training large language models (llms) step 8: model fine tuning
Duración:03:00:57
Steps on training large language models (llms) step 9: model deployment
Duración:05:02:57
Steps on training large language models (llms) step 10: continuous monitoring and improvement
Duración:03:35:48
Training llm for popular use cases
Duración:06:09:19
Training llm for popular use cases sentiment analysis
Duración:04:56:33
Training llm for popular use cases named entity recognition (ner)
Duración:04:40:19
Training llm for popular use cases text summarization
Duración:05:42:07
Training llm for popular use cases question answering
Duración:03:44:52
Training llm for popular use cases language translation
Duración:07:41:24
Training llm for popular use cases text generation
Duración:06:38:50
Training llm for popular use cases topic modeling
Duración:04:28:14
Training llm for popular use cases conversational ai
Duración:04:43:31
Training llm for popular use cases code generation
Duración:06:52:31
Training llm for popular use cases text classification
Duración:07:19:50
Training llm for popular use cases speech recognition
Duración:05:00:24
Training llm for popular use cases image captioning
Duración:06:14:36
Training llm for popular use cases document summarization
Duración:01:10:04
Training llm for popular use cases healthcare applications
Duración:05:41:55
Popular examples of trained large language models (llms) in industry
Duración:04:25:33
Popular examples of trained large language models (llms) in industry natural language processing (nlp) applications
Duración:03:46:12
Popular examples of trained large language models (llms) in industry healthcare and medical text analysis
Duración:04:28:07
Popular examples of trained large language models (llms) in industry financial sentiment analysis
Duración:05:46:04
Popular examples of trained large language models (llms) in industry legal document understanding
Duración:04:04:12
Popular examples of trained large language models (llms) in industry conversational ai and chatbots
Duración:04:24:36
Popular examples of trained large language models (llms) in industry e commerce product recommendations
Duración:05:56:14
Popular examples of trained large language models (llms) in industry educational content generation
Duración:05:00:33
Popular examples of trained large language models (llms) in industry news article summarization
Duración:06:32:14
Dealing with common challenges
Duración:06:06:28
Scaling up: distributed training
Duración:05:43:52
Ensuring ethical and fair use
Duración:04:14:31
Future trends in llms
Duración:04:54:12
Part 2 hardware for llm ai
Duración:00:16:26
Introduction to hardware for llm ai
Duración:03:31:12
Introduction to hardware for llm ai overview of large language models (llms)
Duración:03:49:52
Introduction to hardware for llm ai importance of hardware infrastructure
Duración:05:59:38
Components of hardware for llm ai
Duración:04:15:26
Components of hardware for llm ai central processing units (cpus)
Duración:07:14:52
Components of hardware for llm ai graphics processing units (gpus)
Duración:04:15:14
Components of hardware for llm ai memory systems
Duración:06:45:19
Components of hardware for llm ai storage solutions
Duración:09:14:31
Components of hardware for llm ai networking infrastructure
Duración:03:47:28
Optimizing hardware for llm ai
Duración:04:31:36
Optimizing hardware for llm ai performance optimization
Duración:06:00:14
Optimizing hardware for llm ai scalability and elasticity
Duración:04:40:43
Optimizing hardware for llm ai cost optimization
Duración:08:12:28
Optimizing hardware for llm ai reliability and availability
Duración:04:15:02
Creating on premises hardware for running llm in production
Duración:07:18:36
Creating on premises hardware for running llm in production hardware requirements assessment
Duración:03:30:33
Creating on premises hardware for running llm in production hardware selection
Duración:05:31:50
Creating on premises hardware for running llm in production hardware procurement
Duración:04:44:57
Creating on premises hardware for running llm in production hardware setup and configuration
Duración:05:28:43
Creating on premises hardware for running llm in production testing and optimization
Duración:05:04:09
Creating on premises hardware for running llm in production maintenance and monitoring
Duración:04:49:16
Creating cloud infrastructure or hardware resources for running llm in production
Duración:04:13:07
Creating cloud infrastructure or hardware resources for running llm in production cloud provider selection
Duración:04:24:28
Creating cloud infrastructure or hardware resources for running llm in production resource provisioning
Duración:05:36:04
Creating cloud infrastructure or hardware resources for running llm in production resource configuration
Duración:03:53:07
Creating cloud infrastructure or hardware resources for running llm in production security and access control
Duración:05:40:43
Creating cloud infrastructure or hardware resources for running llm in production scaling and auto scaling
Duración:07:02:21
Creating cloud infrastructure or hardware resources for running llm in production monitoring and optimization
Duración:05:11:36
Hardware overview of openai chatgpt
Duración:03:44:07
Hardware overview of openai chatgpt cpu
Duración:04:07:55
Hardware overview of openai chatgpt gpu
Duración:04:16:38
Hardware overview of openai chatgpt memory
Duración:04:44:36
Hardware overview of openai chatgpt storage
Duración:03:36:21
Steps to create hardware or infrastructure for running lama 2 70b
Duración:05:11:31
Steps to create hardware or infrastructure for running lama 2 70b assess hardware requirements for lama 2 70b
Duración:03:41:45
Steps to create hardware or infrastructure for running lama 2 70b procure hardware components
Duración:04:48:07
Steps to create hardware or infrastructure for running lama 2 70b setup hardware infrastructure
Duración:04:14:14
Steps to create hardware or infrastructure for running lama 2 70b install operating system and dependencies
Duración:05:53:45
Steps to create hardware or infrastructure for running lama 2 70b configure networking
Duración:05:37:50
Steps to create hardware or infrastructure for running lama 2 70b deploy lama 2 70b
Duración:04:17:57
Steps to create hardware or infrastructure for running lama 2 70b testing and optimization
Duración:04:16:09
Popular companies building hardware for running llm
Duración:04:09:00
Popular companies building hardware for running llm nvidia
Duración:03:29:28
Popular companies building hardware for running llm amd
Duración:06:02:45
Popular companies building hardware for running llm intel
Duración:03:21:57
Popular companies building hardware for running llm google
Duración:03:45:50
Popular companies building hardware for running llm amazon web services (aws)
Duración:04:46:09
Comparison: gpu vs cpu for running llm
Duración:04:15:19
Comparison: gpu vs cpu for running llm performance
Duración:04:38:52
Comparison: gpu vs cpu for running llm cost
Duración:05:08:09
Comparison: gpu vs cpu for running llm scalability
Duración:04:12:31
Comparison: gpu vs cpu for running llm specialized tasks
Duración:07:21:09
Comparison: gpu vs cpu for running llm resource utilization
Duración:05:10:36
Comparison: gpu vs cpu for running llm use cases
Duración:04:35:28
Case studies and best practices
Duración:04:59:12
Case studies and best practices real world deployments
Duración:05:04:52
Case studies and best practices industry trends and innovations
Duración:06:28:50
Conclusion summary and key takeaways
Duración:05:37:04
Conclusion future directions
Duración:06:13:07
Glossary
Duración:06:03:36
Bibliography
Duración:07:36:07
Ending Credits
Duración:02:06:45