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 - 11h 5m.
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 - 11h 5m. Author - Et Tu Code. Narrator - Helen Green. Published Date - Monday, 29 January 2024. Copyright - © 2024 Et Tu Code ©.
Language:
English
Opening Credits
Duration:00:02:04
Preface
Duration:00:06:15
Part 1 building your own large language model
Duration:00:00:16
Introduction to language model development
Duration:00:05:54
Basics of natural language processing
Duration:00:03:26
Choosing the right framework
Duration:00:05:04
Collecting and preprocessing data
Duration:00:04:50
Model architecture design
Duration:00:05:29
Evaluation metrics and validation
Duration:00:05:11
Deploying your language model
Duration:00:04:42
Handling ethical and bias considerations
Duration:00:04:33
Optimizing performance and efficiency
Duration:00:04:56
Popular large language models
Duration:00:06:02
Popular large language models gpt 3 (generative pre trained transformer 3)
Duration:00:04:41
Popular large language models bert (bidirectional encoder representations from transformers)
Duration:00:04:03
Popular large language models t5 (text to text transfer transformer)
Duration:00:05:05
Popular large language models xlnet
Duration:00:04:05
Popular large language models roberta (robustly optimized bert approach)
Duration:00:05:21
Popular large language models llama 2
Duration:00:04:28
Popular large language models google's gemini
Duration:00:05:24
Integrating language model with applications
Duration:00:04:44
Continuous improvement and maintenance
Duration:00:03:21
Interpretable ai and explainability
Duration:00:06:26
Challenges and future trends
Duration:00:04:30
Case studies and project examples
Duration:00:04:56
Community and collaboration
Duration:00:04:21
Conclusion
Duration:00:04:55
Basics of natural language processing (nlp)
Duration:00:04:44
Choosing the right architecture
Duration:00:05:17
Data collection and preprocessing
Duration:00:05:20
Hyperparameter tuning
Duration:00:05:21
Transfer learning strategies
Duration:00:05:04
Addressing overfitting and regularization
Duration:00:05:13
Fine tuning for specific tasks
Duration:00:05:31
Steps on training large language models (llms)
Duration:00:03:20
Steps on training large language models (llms) step 1: define your objective
Duration:00:03:50
Steps on training large language models (llms) step 2: data collection and preparation
Duration:00:04:29
Steps on training large language models (llms) step 3: choose a pre trained model or architecture
Duration:00:03:44
Steps on training large language models (llms) step 4: model configuration
Duration:00:04:24
Steps on training large language models (llms) step 5: training process
Duration:00:02:54
Steps on training large language models (llms) step 6: model evaluation
Duration:00:04:44
Steps on training large language models (llms) step 7: hyperparameter tuning
Duration:00:05:50
Steps on training large language models (llms) step 8: model fine tuning
Duration:00:03:00
Steps on training large language models (llms) step 9: model deployment
Duration:00:05:02
Steps on training large language models (llms) step 10: continuous monitoring and improvement
Duration:00:03:35
Training llm for popular use cases
Duration:00:06:09
Training llm for popular use cases sentiment analysis
Duration:00:04:56
Training llm for popular use cases named entity recognition (ner)
Duration:00:04:40
Training llm for popular use cases text summarization
Duration:00:05:42
Training llm for popular use cases question answering
Duration:00:03:44
Training llm for popular use cases language translation
Duration:00:07:41
Training llm for popular use cases text generation
Duration:00:06:38
Training llm for popular use cases topic modeling
Duration:00:04:28
Training llm for popular use cases conversational ai
Duration:00:04:43
Training llm for popular use cases code generation
Duration:00:06:52
Training llm for popular use cases text classification
Duration:00:07:19
Training llm for popular use cases speech recognition
Duration:00:05:00
Training llm for popular use cases image captioning
Duration:00:06:14
Training llm for popular use cases document summarization
Duration:00:01:10
Training llm for popular use cases healthcare applications
Duration:00:05:41
Popular examples of trained large language models (llms) in industry
Duration:00:04:25
Popular examples of trained large language models (llms) in industry natural language processing (nlp) applications
Duration:00:03:46
Popular examples of trained large language models (llms) in industry healthcare and medical text analysis
Duration:00:04:28
Popular examples of trained large language models (llms) in industry financial sentiment analysis
Duration:00:05:46
Popular examples of trained large language models (llms) in industry legal document understanding
Duration:00:04:04
Popular examples of trained large language models (llms) in industry conversational ai and chatbots
Duration:00:04:24
Popular examples of trained large language models (llms) in industry e commerce product recommendations
Duration:00:05:56
Popular examples of trained large language models (llms) in industry educational content generation
Duration:00:05:00
Popular examples of trained large language models (llms) in industry news article summarization
Duration:00:06:32
Dealing with common challenges
Duration:00:06:06
Scaling up: distributed training
Duration:00:05:43
Ensuring ethical and fair use
Duration:00:04:14
Future trends in llms
Duration:00:04:54
Part 2 hardware for llm ai
Duration:00:00:16
Introduction to hardware for llm ai
Duration:00:03:31
Introduction to hardware for llm ai overview of large language models (llms)
Duration:00:03:49
Introduction to hardware for llm ai importance of hardware infrastructure
Duration:00:05:59
Components of hardware for llm ai
Duration:00:04:15
Components of hardware for llm ai central processing units (cpus)
Duration:00:07:14
Components of hardware for llm ai graphics processing units (gpus)
Duration:00:04:15
Components of hardware for llm ai memory systems
Duration:00:06:45
Components of hardware for llm ai storage solutions
Duration:00:09:14
Components of hardware for llm ai networking infrastructure
Duration:00:03:47
Optimizing hardware for llm ai
Duration:00:04:31
Optimizing hardware for llm ai performance optimization
Duration:00:06:00
Optimizing hardware for llm ai scalability and elasticity
Duration:00:04:40
Optimizing hardware for llm ai cost optimization
Duration:00:08:12
Optimizing hardware for llm ai reliability and availability
Duration:00:04:15
Creating on premises hardware for running llm in production
Duration:00:07:18
Creating on premises hardware for running llm in production hardware requirements assessment
Duration:00:03:30
Creating on premises hardware for running llm in production hardware selection
Duration:00:05:31
Creating on premises hardware for running llm in production hardware procurement
Duration:00:04:44
Creating on premises hardware for running llm in production hardware setup and configuration
Duration:00:05:28
Creating on premises hardware for running llm in production testing and optimization
Duration:00:05:04
Creating on premises hardware for running llm in production maintenance and monitoring
Duration:00:04:49
Creating cloud infrastructure or hardware resources for running llm in production
Duration:00:04:13
Creating cloud infrastructure or hardware resources for running llm in production cloud provider selection
Duration:00:04:24
Creating cloud infrastructure or hardware resources for running llm in production resource provisioning
Duration:00:05:36
Creating cloud infrastructure or hardware resources for running llm in production resource configuration
Duration:00:03:53
Creating cloud infrastructure or hardware resources for running llm in production security and access control
Duration:00:05:40
Creating cloud infrastructure or hardware resources for running llm in production scaling and auto scaling
Duration:00:07:02
Creating cloud infrastructure or hardware resources for running llm in production monitoring and optimization
Duration:00:05:11
Hardware overview of openai chatgpt
Duration:00:03:44
Hardware overview of openai chatgpt cpu
Duration:00:04:07
Hardware overview of openai chatgpt gpu
Duration:00:04:16
Hardware overview of openai chatgpt memory
Duration:00:04:44
Hardware overview of openai chatgpt storage
Duration:00:03:36
Steps to create hardware or infrastructure for running lama 2 70b
Duration:00:05:11
Steps to create hardware or infrastructure for running lama 2 70b assess hardware requirements for lama 2 70b
Duration:00:03:41
Steps to create hardware or infrastructure for running lama 2 70b procure hardware components
Duration:00:04:48
Steps to create hardware or infrastructure for running lama 2 70b setup hardware infrastructure
Duration:00:04:14
Steps to create hardware or infrastructure for running lama 2 70b install operating system and dependencies
Duration:00:05:53
Steps to create hardware or infrastructure for running lama 2 70b configure networking
Duration:00:05:37
Steps to create hardware or infrastructure for running lama 2 70b deploy lama 2 70b
Duration:00:04:17
Steps to create hardware or infrastructure for running lama 2 70b testing and optimization
Duration:00:04:16
Popular companies building hardware for running llm
Duration:00:04:09
Popular companies building hardware for running llm nvidia
Duration:00:03:29
Popular companies building hardware for running llm amd
Duration:00:06:02
Popular companies building hardware for running llm intel
Duration:00:03:21
Popular companies building hardware for running llm google
Duration:00:03:45
Popular companies building hardware for running llm amazon web services (aws)
Duration:00:04:46
Comparison: gpu vs cpu for running llm
Duration:00:04:15
Comparison: gpu vs cpu for running llm performance
Duration:00:04:38
Comparison: gpu vs cpu for running llm cost
Duration:00:05:08
Comparison: gpu vs cpu for running llm scalability
Duration:00:04:12
Comparison: gpu vs cpu for running llm specialized tasks
Duration:00:07:21
Comparison: gpu vs cpu for running llm resource utilization
Duration:00:05:10
Comparison: gpu vs cpu for running llm use cases
Duration:00:04:35
Case studies and best practices
Duration:00:04:59
Case studies and best practices real world deployments
Duration:00:05:04
Case studies and best practices industry trends and innovations
Duration:00:06:28
Conclusion summary and key takeaways
Duration:00:05:37
Conclusion future directions
Duration:00:06:13
Glossary
Duration:00:06:03
Bibliography
Duration:00:07:36
Ending Credits
Duration:00:02:06