Introduction to large language models
AI技術の魅力を伝える記事の中で、Google Cloud Skills Boostというコースについて紹介しています。このコースは、大規模言語モデル（LLMs）や生成AIといった深層学習技術に焦点を当てており、その魅力を伝えるための素晴らしい機会です。
また、LLMsやGenerative AIの重要性や活用方法について詳しく説明した動画もご紹介しています。この動画では、Prompt TuningやGenAI開発ツールなど、LLMsに関するさまざまな情報が提供されています。
さらに、Google Cloud Techという購読サービスについても触れており、最新の技術トレンドや情報にアクセスするための手段としておすすめしています。
このような記事は、AI技術への興味を持つ読者にとって非常に魅力的であり、将来的なキャリアやビジネス展開に役立つ知識を提供することができます。是非、上記のリンク先をチェックしてみてください。
2.20: he got the definition of hyperparameters wrong. In ML, there is a clear distinction between parameters and hyperparameters.
YOU GOTTA TELL EM! TELL EVERYONE, SoiLLM's green is made out of. . . PEOPLE!!
Tutorial de LLM en español, por si interesa: https://www.youtube.com/watch?v=IQAONsP_q-8
Kaylah Branch
🙄🙄🙄🙄
text classificaiton
13:19 fine tuning not realsitic
Hey there! AI is definitely becoming more prevalent on Facebook. I've noticed more personalized content and ads powered by AI algorithms. It's amazing how AI enhances our social media experience."
Thankyou for this.
It's very clear to understand LLM, thank you
this was bestowed upon us by our true creators to speed up the process so they can come in and enslave us again😂🎉
Excelente Google cloud
Creating a prompt seems more of a "Craft" than engineering.
passed
i love it
Cool!
RIP Bard, gone so young..
0:57 What do pre-trainned and fine-tuned llms means? Good analogy with dogs.
This is one of the educative sessions I've come across
🎯 Key Takeaways for quick navigation:
00:29 🧠 Large Language Models (LLMs) and generative AI are both subsets of deep learning, with LLMs being general-purpose models that can be pre-trained and fine-tuned for specific tasks.
01:56 📊 Large language models are characterized by their enormous size in terms of training data sets and parameter counts, offering capabilities across various industries with minimal domain-specific training data.
03:22 💡 Large language models enable multi-tasking, require minimal field training data, and their performance improves with additional data and parameters.
04:21 🔄 Example: Google's Pathways Language Model (PaLM), a 540 billion-parameter model, showcases the continuous advancement and scalability of large language models.
06:44 🛠️ LLM development simplifies the process, requiring prompt design rather than expert-level knowledge or extensive training data, unlike traditional machine learning.
07:41 🤖 Generative QA allows models like Bard to answer questions without domain-specific knowledge, showcasing the potential of large language models in various applications.
09:08 🎯 Prompt design and engineering are crucial in natural language processing, tailoring prompts for specific tasks and improving model performance.
11:03 💬 Different types of large language models, such as generic, instruction-tuned, and dialogue-tuned, require varied prompting approaches for optimal performance.
12:01 💼 Task-specific tuning enhances LLM reliability, offering domain-specific models for sentiment analysis, vision tasks, and other applications.
13:27 💰 Parameter-efficient tuning methods (PETM) enable customization of large language models on custom data without altering the base model, providing cost-effective tuning solutions.
Made with HARPA AI
Was this long? YES.
Did I learn? YES.
Did I want to sleep? YES.
Did I sleep before the end? NO.
A WIN
We are creating our own prison…
Nice one!
Very Informative – Thanks for sharing 😊 prompt design and prompt engineering would take make the conversation more realistic and accurate.
Thank for sharing👍
Very comprehensive video! Thank you guys!
Minor Correction @ 2:14. "In ML, parameters are often called hyperparameters." In ML, parameters and hyperparameters can exist simultaneously and serve two different purposes. One can think of hyperparameters as the set of knobs that the designer has direct influence to change as they see fit (whether algorithmically or manually). As for the parameters of a model, one can think of it as the set of knobs that are learned directly from the data. For hyperparameters, you specify them prior to the training step; while the training step proceeds, the parameters of the model are being learned.