Large-Scale Language Models for Everyone LLM Part 3 - Building AI Applications with Google Gemini API, OpenAI API, and Gemma

This course will teach you the concept of the Google Gemini model and how to use the Gemini API, and create various AI applications using Streamlit.

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수강기한 무제한

openAI API
openAI API
ChatGPT
ChatGPT
gemini
gemini
Gemma
Gemma
multimodal
multimodal
openAI API
openAI API
ChatGPT
ChatGPT
gemini
gemini
Gemma
Gemma
multimodal
multimodal

강의상세_배울수있는것_타이틀

  • Structure and Features of the Google Gemini Model

  • How to build multimodal AI applications using Google Gemini API

  • How to build AI applications with Streamlit

  • The concept of the Gemma model and how to fine-tune the Gemma model on the dataset I want

  • How to create various AI applications using the OpenAI API

The latest Multimodal LLM Gemini released by Google!

After learning the concepts and API usage of the multimodal LLM Gemini, you will create your own AI application using Streamlit.

I recommend this to these people

Anyone who wants to learn the principles and usage of the Multimodal LLM Gemini model

Anyone who wants to develop AI applications using Streamlit

Anyone who wants to compare and review various LLM models other than GPT

Q&A 💬

Q. What is Google Gemini?

Google Gemini is a state-of-the-art multimodal large-scale language model developed by Google DeepMind. Designed as a successor to its predecessors, LaMDA and PaLM2, Gemini comes in three versions: Gemini Ultra, Gemini Pro, and Gemini Nano , designed to perform a variety of tasks on a variety of platforms, from data centers to mobile devices.

Introduced on December 6, 2023, Gemini is positioned as a competitor to OpenAI's GPT-4 and offers a Gemini service, a generative AI chatbot similar to ChatGPT.

Because it is taught multimodally, you can understand and create images, audio, and videos in addition to the text that traditional LLMs cover .

Q. Is player knowledge required?

This lecture [Large Language Model for Everyone LLM Part 3 - Building AI Applications with Google Gemini API, OpenAI API, and Gemma] assumes prior knowledge of Python, natural language processing, LLM, OpenAI API, and LangChain . Therefore, the prerequisite lecture [Large Language Model for Everyone LLM (Large Language Model) Part 1 - Fine-Tuning Llama 2 ] Please take the course [Large Language Model for Everyone LLM (Large Language Model) Part 2 - Building Your Own ChatGPT with LangChain] first.

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학습 대상은 누구일까요?

  • Anyone who wants to learn the structure and features of the Google Gemini model

  • Anyone who wants to create a multimodal AI application using Google Gemini API

  • Anyone who wants to learn how to create AI applications using Streamlit

  • Those who want to get a job related to deep learning research

  • Anyone who wants to conduct research related to artificial intelligence/deep learning

  • Those preparing for graduate school in artificial intelligence (AI)

선수 지식, 필요할까요?

  • Experience using Python

  • Understanding of basic data structures such as List and Dictionary in Python

  • Experience of attending the pre-course [Large Language Model LLM Part 1 - Try Llama 2 Fine-Tuning]

  • Experience of attending the pre-course [Large Language Model for Everyone LLM (Large Language Model) Part 2 - Creating My Own ChatGPT with LangChain]

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      The lecture was fun because it was practice-oriented, but the explanations were too fast, making it very difficult to follow along.

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