If you’re interested to learn large language models (LLMs), you’re in the right place. LLMs are all the rage these days, and for good reason. They’re incredibly powerful tools that can be used to do a wide range of things, from generating text to translating languages to writing code.
LLMs can be used to build a variety of applications, such as chatbots, virtual assistants, and translation tools. They can also be used to improve the performance of existing NLP tasks, such as text summarization and machine translation.
In this blog post, we are going to share the top 10 YouTube videos for learning about LLMs. These videos cover everything from the basics of how LLMs work to how to build and deploy your own LLM. Experts in the field teach these concepts, giving you the assurance of receiving the latest information.
1. LLM for real-world Applications
Custom LLMs are trained on your specific data. This means that they can be tailored to your specific needs. For example, you could train a custom LLM on your customer data to improve your customer service experience.
LLMs are a powerful tool that can be used to improve your business in a number of ways. If you’re not already using LLMs in your business, I encourage you to check out the video above to learn more about their potential applications.
In this video, you will learn about the following:
- What are LLMs and how do they work?
- What are the different types of LLMs?
- What are some of the real-world applications of LLMs?
- How can you get started with using LLMs in your own work?
2. Emerging Architectures for LLM Applications
In this video, you will learn about the latest approaches to building custom LLM applications. This means that you can build an LLM that is tailored to your specific needs. You will also learn about the different tools and technologies that are available, such as LangChain.
Applications like Bard, ChatGPT, Midjourney, and DallE have entered some applications like content generation and summarization. However, there are inherent challenges for a lot of tasks that require a deeper understanding of trade-offs like latency, accuracy, and consistency of responses.
Any serious applications of LLMs require an understanding of nuances in how LLMs work, embeddings, vector databases, retrieval augmented generation (RAG), orchestration frameworks, and more.
In this video, you will learn about the following:
- What are the challenges of using LLMs in real-world applications?
- What are some of the emerging architectures for LLM applications?
- How can these architectures be used to overcome the challenges of using LLMs in real-world applications?
3. Vector Similarity Search
This video explains what vector databases are and how they can be used for vector similarity searches. Vector databases are a type of database that stores data in the form of vectors. Vectors are mathematical objects that represent the direction and magnitude of a force or quantity.
A vector similarity search is the process of finding similar vectors in a vector database. Vector similarity search can be used for a variety of tasks, such as image retrieval, text search, and recommendation systems.
In this video, you will learn about the following:
- What are vector databases?
- What is vector similarity search?
- How can vector databases be used for vector similarity searches?
- What are some of the benefits of using vector databases for vector similarity searches?
4. Agents in LangChain
This video explains what LangChain agents are and how they can be used to build AI applications. LangChain agents are a type of artificial intelligence that can be used to build AI applications. They are based on large language models (LLMs), which are a type of artificial intelligence that can generate and understand human language.
Link to video – Agents in LangChain
In this video, you will learn about the following:
- What are LangChain agents?
- How can LangChain agents be used to build AI applications?
- What are some of the benefits of using LangChain agents to build AI applications?
5. Build your own ChatGPT
This video shows how to use the ChatGPT API to build your own AI application. ChatGPT is a large language model (LLM) that can be used to generate text, translate languages, and answer questions in an informative way.
Link to video: Build your own ChatGPT
In this video, you will learn about the following:
- What is the ChatGPT API?
- How can the ChatGPT API be used to build AI applications?
- What are some of the benefits of using the ChatGPT API to build AI applications?
6. The Power of Embeddings with Vector Search
Embeddings are a powerful tool for representing data in an easy-to-understand way for machine learning algorithms. Vector search is a technique for finding similar vectors in a database. Together, embeddings and vector search can be used to solve a wide range of problems, such as image retrieval, text search, and recommendation systems.
Key learning outcomes:
- What are embeddings and how do they work?
- What is vector search and how is it used?
- How can embeddings and vector search be used to solve real-world problems?
7. AI in Emergency Medicine
Artificial intelligence (AI) is rapidly transforming the field of emergency medicine. AI is being used to develop new diagnostic tools, improve the efficiency of care delivery, and even predict patient outcomes.
Key learning outcomes:
- What are the latest advances in AI in emergency medicine?
- How is AI being used to improve patient care?
- What are the challenges and opportunities of using AI in emergency medicine?
8. Generative AI Trends, Ethics, and Societal Impact
Generative AI is a type of AI that can create new content, such as text, images, and music. Generative AI is rapidly evolving and has the potential to revolutionize many industries. However, it also raises important ethical and societal questions.
Key learning outcomes:
- What are the latest trends in generative AI?
- What are the potential benefits and risks of generative AI?
- How can we ensure that generative AI is used responsibly and ethically?
9. Hugging Face + LangKit
Hugging Face and LangKit are two popular open-source libraries for natural language processing (NLP). Hugging Face provides a variety of pre-trained NLP models, while LangKit provides a set of tools for training and deploying NLP models.
Key learning outcomes:
- What are Hugging Face and LangKit?
- How can Hugging Face and LangKit be used to build NLP applications?
- What are some of the benefits of using Hugging Face and LangKit?
10. Master ChatGPT for Data Analysis and Visualization!
ChatGPT is a large language model that can be used for a variety of tasks, including data analysis and visualization. In this video, you will learn how to use ChatGPT to perform common data analysis tasks, such as data cleaning, data exploration, and data visualization.
Key learning outcomes:
- How to use ChatGPT to perform data analysis tasks
- How to use ChatGPT to create data visualizations
- How to use ChatGPT to communicate your data findings
Visit our YouTube channel to learn large language model
LLMs can help you build your own large language models, like ChatGPT. They can also help you use custom language models to grow your business. For example, you can use custom language models to improve customer service, develop new products and services, automate marketing and sales tasks, and improve the quality of your content.
So, what are you waiting for? Start learning about LLMs today!
