This blog lists several YouTube channels that can help you get started with LLMs, generative AI, prompt engineering, and more.
Large language models like GPT-3.5 have revolutionized the field of natural language processing. These models, trained on massive datasets, can generate coherent and contextually relevant text, making them invaluable across numerous applications.
These YouTube videos will help you learn large language models
Learning about them has become increasingly important in today’s rapidly evolving technological landscape. These models are at the forefront of advancements in artificial intelligence and natural language processing. Understanding their capabilities and limitations empowers individuals and professionals across various fields.
Top YouTube Channels to Learn About Large Language Models
Databricks
From the basics of foundation models to fundamental concepts, you can find a ton of useful tutorials and talks that can help you get started with LLMS. Learn about fine-tuning, deployment, and other related concepts with this channel.
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Data Science Dojo
Want to get started with the basics of large language model basics? Want to fine-tune your LLM application? Want to build your own ChatGPT? If so, then hop on to this channel now because it covers a number of tutorials, master classes, and free crash courses pertaining to large language models.
Learn about Llama Index, LangChain, Redis, Retrieval Augmented Generation, AI observability, and more with this channel. Subscribe now and start learning.
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AssemblyAI
Learn about Llama index, vector databases and LangChain, explore how to build your own coding assistant with ChatGPT, and apply large language models to audio data with AssemblyAI. This channel offers plentiful learning tutorials within the domain of large language models.
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FreeCodeCamp
FreeCodeCamp offers a wide range of tutorials, including how to build large language models with Python, prompt engineering for web developers, a LangChain course, and more. This channel can help you to get started with the basics.
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Mathew Berman
From artificial intelligence to generative art, this channel sheds light on several significant areas including AI art, ChatGPT, large language Models, machine learning, technology, and coding. Subscribe now and start learning.
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IBM Technology
This channel includes several talks and tutorials pertaining to machine learning and generative AI. From useful tutorials like building a Chabot with AI to insightful talks like the rise of generative AI, this channel can help you navigate your learning path.
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Yannic Kilcher
Talks to short tutorials, this channel offers a number of resources to learn about large language models, llama 2, ReST for language modeling, retentive networks, and more that can assist you in building your LLM knowledge base.
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Nicholas Renotte
Nicholas shares practical ways to get started with data science, machine learning, and deep learning using a bunch of different tools but mainly Python and Javascript. The channel includes many useful talks like breaking down the generative AI stack, building an AutoGPT, and using Llama 2 70B to rebuild GPT Banker.
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Eve on Tech
Eye on Tech focuses on the latest business technology and IT topics, including AI, DevOps, security, networking, cloud, storage, and more. This channel covers a number of useful talks like the introduction to foundation models, AI buzzwords, conversational AI versus generative AI, and more that can help you get started with the basics.
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Start learning large language models today!
Large language models (LLMs) are a type of artificial intelligence (AI) model that can generate and understand text. They are trained on massive datasets of text and code, which allows them to learn the statistical relationships between words and phrases.
The field of LLMs is rapidly growing, and new models are being developed all the time. In recent years, there have been a number of breakthroughs in LLM technology, including:
- The development of new training algorithms that allow LLMs to be trained on much larger datasets than ever before.
- The development of new hardware architectures that can support the training and inference of LLMs more efficiently.
- The release of open-source LLM models, which has made LLMs more accessible to researchers and developers.
As a result of these advances, LLMs are becoming increasingly powerful and capable. By understanding LLMs, you can position yourself to take advantage of the opportunities that they create.
