Demystifying LLM, AI Mathematics, and Hardware Infra
Et Tu Code
This ebook is a comprehensive guide to understanding Large Language Models (LLMs), AI Mathematics, and its Hardware Infrastructure. It covers the basics of Natural Language Processing (NLP), choosing the right framework, collecting and preprocessing data, model architecture design, training and fine-tuning, evaluation metrics and validation, deploying your language model, and more. The book also delves into ethical and bias considerations, optimizing performance and efficiency, popular LLMs, integrating with applications, scaling and distributed training, continuous improvement and maintenance, interpretable AI and explainability, challenges and future trends, case studies and project examples, community and collaboration, and a comprehensive introduction to mathematics in AI.
The book provides an in-depth look at the mathematical foundations of LLMs, including essential mathematical concepts, statistics for AI, optimization in AI, linear algebra in AI, calculus for machine learning, probability theory in AI, advanced topics in mathematics for AI, and more. It also covers how to implement AI mathematics concepts with Python, popular Python packages for implementing AI mathematics, applications of mathematics and statistics in AI, and the hardware overview of OpenAI ChatGPT. The book is designed to help readers understand the complex world of LLMs, AI Mathematics, and Hardware Infra, and how they can be used to create innovative applications and solutions.
Whether you're a developer, researcher, or simply curious about the latest advancements in AI, this guide will provide you with a comprehensive understanding of the field.
Duration - 12h 19m.
Author - Et Tu Code.
Narrator - Helen Green.
Published Date - Monday, 15 January 2024.
Copyright - © 2024 Et Tu Code ©.
Location:
United States
Description:
This ebook is a comprehensive guide to understanding Large Language Models (LLMs), AI Mathematics, and its Hardware Infrastructure. It covers the basics of Natural Language Processing (NLP), choosing the right framework, collecting and preprocessing data, model architecture design, training and fine-tuning, evaluation metrics and validation, deploying your language model, and more. The book also delves into ethical and bias considerations, optimizing performance and efficiency, popular LLMs, integrating with applications, scaling and distributed training, continuous improvement and maintenance, interpretable AI and explainability, challenges and future trends, case studies and project examples, community and collaboration, and a comprehensive introduction to mathematics in AI. The book provides an in-depth look at the mathematical foundations of LLMs, including essential mathematical concepts, statistics for AI, optimization in AI, linear algebra in AI, calculus for machine learning, probability theory in AI, advanced topics in mathematics for AI, and more. It also covers how to implement AI mathematics concepts with Python, popular Python packages for implementing AI mathematics, applications of mathematics and statistics in AI, and the hardware overview of OpenAI ChatGPT. The book is designed to help readers understand the complex world of LLMs, AI Mathematics, and Hardware Infra, and how they can be used to create innovative applications and solutions. Whether you're a developer, researcher, or simply curious about the latest advancements in AI, this guide will provide you with a comprehensive understanding of the field. Duration - 12h 19m. Author - Et Tu Code. Narrator - Helen Green. Published Date - Monday, 15 January 2024. Copyright - © 2024 Et Tu Code ©.
Language:
English
Opening Credits
Duración:00:02:06
Preface
Duración:00:04:33
Part 1 llm
Duración:00:00:15
Introduction to language model development
Duración:00:05:54
Basics of natural language processing
Duración:00:03:26
Choosing the right framework
Duración:00:05:04
Collecting and preprocessing data
Duración:00:04:50
Model architecture design
Duración:00:05:29
Training and fine tuning
Duración:00:05:57
Evaluation metrics and validation
Duración:00:05:11
Deploying your language model
Duración:00:04:42
Fine tuning for specific use cases
Duración:00:06:50
Handling ethical and bias considerations
Duración:00:04:33
Optimizing performance and efficiency
Duración:00:04:56
Popular large language models
Duración:00:06:02
Popular large language models gpt 3 (generative pre trained transformer 3)
Duración:00:04:41
Popular large language models bert (bidirectional encoder representations from transformers)
Duración:00:04:03
Popular large language models t5 (text to text transfer transformer)
Duración:00:05:05
Popular large language models xlnet
Duración:00:04:05
Popular large language models roberta (robustly optimized bert approach)
Duración:00:05:21
Popular large language models llama 2
Duración:00:04:28
Popular large language models google's gemini
Duración:00:05:24
Integrating language model with applications
Duración:00:04:44
Scaling and distributed training
Duración:00:04:22
Continuous improvement and maintenance
Duración:00:03:21
Interpretable ai and explainability
Duración:00:06:26
Challenges and future trends
Duración:00:04:30
Case studies and project examples
Duración:00:04:56
Community and collaboration
Duración:00:04:21
Conclusion
Duración:00:04:55
Part 2 ai maths
Duración:00:00:15
Introduction to mathematics in ai
Duración:00:05:43
Essential mathematical concepts
Duración:00:05:48
Statistics for ai
Duración:00:04:20
Optimization in ai
Duración:00:10:14
Linear algebra in ai
Duración:00:04:55
Calculus for machine learning
Duración:00:04:50
Probability theory in ai
Duración:00:05:17
Advanced topics in mathematics for ai
Duración:00:06:25
Mathematical foundations of neural networks
Duración:00:04:45
Mathematics behind popular machine learning algorithms
Duración:00:06:15
Mathematics behind popular machine learning algorithms linear regression
Duración:00:03:09
Mathematics behind popular machine learning algorithms logistic regression
Duración:00:04:12
Mathematics behind popular machine learning algorithms decision trees
Duración:00:04:28
Mathematics behind popular machine learning algorithms random forests
Duración:00:05:59
Mathematics behind popular machine learning algorithms support vector machines (svm)
Duración:00:04:54
Mathematics behind popular machine learning algorithms k nearest neighbors (knn)
Duración:00:05:47
Mathematics behind popular machine learning algorithms k means clustering
Duración:00:04:38
Mathematics behind popular machine learning algorithms principal component analysis (pca)
Duración:00:04:31
Mathematics behind popular machine learning algorithms neural networks
Duración:00:06:23
Mathematics behind popular machine learning algorithms gradient boosting
Duración:00:05:22
Mathematics behind popular machine learning algorithms recurrent neural networks (rnn)
Duración:00:05:10
Mathematics behind popular machine learning algorithms long short term memory (lstm)
Duración:00:04:20
Mathematics behind popular machine learning algorithms gradient descent
Duración:00:05:46
Implementing ai mathematics concepts with python
Duración:00:05:19
Implementing ai mathematics concepts with python linear regression implementation
Duración:00:04:23
Implementing ai mathematics concepts with python logistic regression implementation
Duración:00:03:44
Implementing ai mathematics concepts with python decision trees implementation
Duración:00:04:32
Implementing ai mathematics concepts with python random forests implementation
Duración:00:05:12
Implementing ai mathematics concepts with python support vector machines (svm) implementation
Duración:00:05:42
Implementing ai mathematics concepts with python neural networks implementation
Duración:00:08:28
Implementing ai mathematics concepts with python k means clustering implementation
Duración:00:05:29
Implementing ai mathematics concepts with python principal component analysis (pca) implementation
Duración:00:05:08
Implementing ai mathematics concepts with python gradient descent implementation
Duración:00:05:28
Implementing ai mathematics concepts with python recurrent neural networks (rnn) implementation
Duración:00:05:58
Implementing ai mathematics concepts with python long short term memory (lstm) implementation
Duración:00:05:40
Implementing ai mathematics concepts with python gradient boosting implementation
Duración:00:08:48
Popular python packages for implementing ai mathematics
Duración:00:08:09
Popular python packages for implementing ai mathematics numpy
Duración:00:04:03
Popular python packages for implementing ai mathematics scipy
Duración:00:05:38
Popular python packages for implementing ai mathematics pandas
Duración:00:05:13
Popular python packages for implementing ai mathematics sympy
Duración:00:06:00
Popular python packages for implementing ai mathematics matplotlib
Duración:00:05:48
Popular python packages for implementing ai mathematics seaborn
Duración:00:04:09
Popular python packages for implementing ai mathematics scikit learn
Duración:00:06:22
Popular python packages for implementing ai mathematics statsmodels
Duración:00:05:20
Popular python packages for implementing ai mathematics tensorflow
Duración:00:07:27
Popular python packages for implementing ai mathematics pytorch
Duración:00:08:40
Applications of mathematics and statistics in ai
Duración:00:07:01
Mathematics in computer vision
Duración:00:06:19
Mathematics in natural language processing
Duración:00:05:17
Mathematics in reinforcement learning
Duración:00:06:08
Conclusion: building a strong mathematical foundation for ai
Duración:00:03:50
Part 3 hardware
Duración:00:00:15
Introduction to hardware for llm ai
Duración:00:03:31
Introduction to hardware for llm ai importance of hardware infrastructure
Duración:00:05:59
Components of hardware for llm ai
Duración:00:04:15
Components of hardware for llm ai central processing units (cpus)
Duración:00:07:14
Components of hardware for llm ai graphics processing units (gpus)
Duración:00:04:15
Components of hardware for llm ai memory systems
Duración:00:06:45
Components of hardware for llm ai storage solutions
Duración:00:09:14
Components of hardware for llm ai networking infrastructure
Duración:00:03:47
Optimizing hardware for llm ai
Duración:00:04:31
Optimizing hardware for llm ai performance optimization
Duración:00:06:00
Optimizing hardware for llm ai scalability and elasticity
Duración:00:04:40
Optimizing hardware for llm ai cost optimization
Duración:00:08:12
Optimizing hardware for llm ai reliability and availability
Duración:00:04:15
Creating on premises hardware for running llm in production
Duración:00:07:18
Creating on premises hardware for running llm in production hardware requirements assessment
Duración:00:03:30
Creating on premises hardware for running llm in production hardware selection
Duración:00:05:31
Creating on premises hardware for running llm in production hardware procurement
Duración:00:04:44
Creating on premises hardware for running llm in production hardware setup and configuration
Duración:00:05:28
Creating on premises hardware for running llm in production testing and optimization
Duración:00:05:04
Creating on premises hardware for running llm in production maintenance and monitoring
Duración:00:04:49
Creating cloud infrastructure or hardware resources for running llm in production
Duración:00:04:13
Creating cloud infrastructure or hardware resources for running llm in production cloud provider selection
Duración:00:04:24
Creating cloud infrastructure or hardware resources for running llm in production resource provisioning
Duración:00:05:36
Creating cloud infrastructure or hardware resources for running llm in production resource configuration
Duración:00:03:53
Creating cloud infrastructure or hardware resources for running llm in production security and access control
Duración:00:05:40
Creating cloud infrastructure or hardware resources for running llm in production scaling and auto scaling
Duración:00:07:02
Creating cloud infrastructure or hardware resources for running llm in production monitoring and optimization
Duración:00:05:11
Hardware overview of openai chatgpt
Duración:00:03:44
Hardware overview of openai chatgpt cpu
Duración:00:04:07
Hardware overview of openai chatgpt gpu
Duración:00:04:16
Hardware overview of openai chatgpt memory
Duración:00:04:44
Hardware overview of openai chatgpt storage
Duración:00:03:36
Steps to create hardware or infrastructure for running lama 2 70b
Duración:00:05:11
Steps to create hardware or infrastructure for running lama 2 70b assess hardware requirements for lama 2 70b
Duración:00:03:41
Steps to create hardware or infrastructure for running lama 2 70b procure hardware components
Duración:00:04:48
Steps to create hardware or infrastructure for running lama 2 70b setup hardware infrastructure
Duración:00:04:14
Steps to create hardware or infrastructure for running lama 2 70b install operating system and dependencies
Duración:00:05:53
Steps to create hardware or infrastructure for running lama 2 70b configure networking
Duración:00:05:37
Steps to create hardware or infrastructure for running lama 2 70b deploy lama 2 70b
Duración:00:04:17
Steps to create hardware or infrastructure for running lama 2 70b testing and optimization
Duración:00:04:16
Popular companies building hardware for running llm
Duración:00:04:09
Popular companies building hardware for running llm nvidia
Duración:00:03:29
Popular companies building hardware for running llm amd
Duración:00:06:02
Popular companies building hardware for running llm intel
Duración:00:03:21
Popular companies building hardware for running llm google
Duración:00:03:45
Popular companies building hardware for running llm amazon web services (aws)
Duración:00:04:46
Comparison: gpu vs cpu for running llm
Duración:00:04:15
Comparison: gpu vs cpu for running llm performance
Duración:00:04:38
Comparison: gpu vs cpu for running llm cost
Duración:00:05:08
Comparison: gpu vs cpu for running llm scalability
Duración:00:04:12
Comparison: gpu vs cpu for running llm specialized tasks
Duración:00:07:21
Comparison: gpu vs cpu for running llm resource utilization
Duración:00:05:10
Comparison: gpu vs cpu for running llm use cases
Duración:00:04:35
Case studies and best practices
Duración:00:04:59
Case studies and best practices real world deployments
Duración:00:05:04
Case studies and best practices industry trends and innovations
Duración:00:06:28
Conclusion summary and key takeaways
Duración:00:05:37
Conclusion future directions
Duración:00:06:13
Glossary
Duración:00:04:43
Bibliography
Duración:00:06:38
Ending Credits
Duración:00:02:09