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
Duration:00:02:06
Preface
Duration:00:04:33
Part 1 llm
Duration:00:00:15
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
Training and fine tuning
Duration:00:05:57
Evaluation metrics and validation
Duration:00:05:11
Deploying your language model
Duration:00:04:42
Fine tuning for specific use cases
Duration:00:06:50
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
Scaling and distributed training
Duration:00:04:22
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
Part 2 ai maths
Duration:00:00:15
Introduction to mathematics in ai
Duration:00:05:43
Essential mathematical concepts
Duration:00:05:48
Statistics for ai
Duration:00:04:20
Optimization in ai
Duration:00:10:14
Linear algebra in ai
Duration:00:04:55
Calculus for machine learning
Duration:00:04:50
Probability theory in ai
Duration:00:05:17
Advanced topics in mathematics for ai
Duration:00:06:25
Mathematical foundations of neural networks
Duration:00:04:45
Mathematics behind popular machine learning algorithms
Duration:00:06:15
Mathematics behind popular machine learning algorithms linear regression
Duration:00:03:09
Mathematics behind popular machine learning algorithms logistic regression
Duration:00:04:12
Mathematics behind popular machine learning algorithms decision trees
Duration:00:04:28
Mathematics behind popular machine learning algorithms random forests
Duration:00:05:59
Mathematics behind popular machine learning algorithms support vector machines (svm)
Duration:00:04:54
Mathematics behind popular machine learning algorithms k nearest neighbors (knn)
Duration:00:05:47
Mathematics behind popular machine learning algorithms k means clustering
Duration:00:04:38
Mathematics behind popular machine learning algorithms principal component analysis (pca)
Duration:00:04:31
Mathematics behind popular machine learning algorithms neural networks
Duration:00:06:23
Mathematics behind popular machine learning algorithms gradient boosting
Duration:00:05:22
Mathematics behind popular machine learning algorithms recurrent neural networks (rnn)
Duration:00:05:10
Mathematics behind popular machine learning algorithms long short term memory (lstm)
Duration:00:04:20
Mathematics behind popular machine learning algorithms gradient descent
Duration:00:05:46
Implementing ai mathematics concepts with python
Duration:00:05:19
Implementing ai mathematics concepts with python linear regression implementation
Duration:00:04:23
Implementing ai mathematics concepts with python logistic regression implementation
Duration:00:03:44
Implementing ai mathematics concepts with python decision trees implementation
Duration:00:04:32
Implementing ai mathematics concepts with python random forests implementation
Duration:00:05:12
Implementing ai mathematics concepts with python support vector machines (svm) implementation
Duration:00:05:42
Implementing ai mathematics concepts with python neural networks implementation
Duration:00:08:28
Implementing ai mathematics concepts with python k means clustering implementation
Duration:00:05:29
Implementing ai mathematics concepts with python principal component analysis (pca) implementation
Duration:00:05:08
Implementing ai mathematics concepts with python gradient descent implementation
Duration:00:05:28
Implementing ai mathematics concepts with python recurrent neural networks (rnn) implementation
Duration:00:05:58
Implementing ai mathematics concepts with python long short term memory (lstm) implementation
Duration:00:05:40
Implementing ai mathematics concepts with python gradient boosting implementation
Duration:00:08:48
Popular python packages for implementing ai mathematics
Duration:00:08:09
Popular python packages for implementing ai mathematics numpy
Duration:00:04:03
Popular python packages for implementing ai mathematics scipy
Duration:00:05:38
Popular python packages for implementing ai mathematics pandas
Duration:00:05:13
Popular python packages for implementing ai mathematics sympy
Duration:00:06:00
Popular python packages for implementing ai mathematics matplotlib
Duration:00:05:48
Popular python packages for implementing ai mathematics seaborn
Duration:00:04:09
Popular python packages for implementing ai mathematics scikit learn
Duration:00:06:22
Popular python packages for implementing ai mathematics statsmodels
Duration:00:05:20
Popular python packages for implementing ai mathematics tensorflow
Duration:00:07:27
Popular python packages for implementing ai mathematics pytorch
Duration:00:08:40
Applications of mathematics and statistics in ai
Duration:00:07:01
Mathematics in computer vision
Duration:00:06:19
Mathematics in natural language processing
Duration:00:05:17
Mathematics in reinforcement learning
Duration:00:06:08
Conclusion: building a strong mathematical foundation for ai
Duration:00:03:50
Part 3 hardware
Duration:00:00:15
Introduction to hardware for llm ai
Duration:00:03:31
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:00:04:43
Bibliography
Duration:00:06:38
Ending Credits
Duration:00:02:09