Resource of free step by step video how to guides to get you started with machine learning.
Monday, March 11, 2024
Tutorial: Bild Chat App with Your Images using Gemini Pro Vision Model
Ever wish you could have a conversation with a photograph? Now you practically can! Learn how to use Google's powerful Gemini Pro Vision AI to ask your images questions, get creative insights, and have fun. In this tutorial, you'll learn how to make your images reveal surprising insights, answer your questions, and inspire creative ideas. Hit that like button and subscribe for more mind-blowing AI adventures! ⚙️ Projects Files used in this tutorial: 1. Streamlit application: ๐ https://github.com/atef-ataya/GeminiVisionProChatWithAnImage 2. Jupyter Notebook file: ๐ ⚙️ Related YouTube Tutorials: 1. Unleash Google Gemini: A Comprehensive LangChain Tutorial ๐ https://youtu.be/LKaHYkLTyTM?si=fnH6_MpIFllEkPYV 2. Building a Question Answering / Chat Application Using OpenAI, Pinecone, & LangChain: ๐ https://youtu.be/CLOsuciiPL4?si=zxIzXzawoUSLOwYu ⚙️ Github Project for you to follow along in this tutorial: 1. Chat With Wikipedia (Streamlit Project) ๐ https://github.com/atef-ataya/ChatAppWithWikipedia 2. Build your own Wikipedia Chatbot with Streamlit & LangChain: ๐https://youtu.be/2j8Ns1UCatI?si=KkZ4xamHj2q1vxq7 3. LangChain Tutorials: ๐ https://github.com/atef-ataya/LangChain-Tutorial ⚙️ Medium Articles: 1. LangChain Tutorials: ๐ https://medium.com/ai-advances/langchain-and-googles-gemini-pro-and-pro-vision-models-a4e6e8565000 TIMESTAMPS: 00:06 Intro 01:20 Jupyter Notebook Prototype 08:05 Building the application using Streamlit ⚡️ Social Media: ๐ Twitter: / atayaatef ๐ Github: https://github.com/atef-ataya ๐ Medium: / atef.ataya ⚙️ Links: ๐ LangChain documentation: https://python.langchain.com/docs/get_started/introduction ๐ Google API: https://aistudio.google.com/app/apikey #AI #ArtificialIntelligence #ImageAnalysis #ComputerVision #MachineLearning #GoogleAI #Tech #Innovation #GeminiProVision #GoogleGemini #Tutorial #HowTo #LearnAI #imageChat #Streamlit #ConversationalAI #GenerativeAI #LLM #LargeLanguageModel #NLP
Labels:
ArtificialIntelligence,
ComputerVision,
GeminiProVision,
generativeai,
GoogleGemini,
ImageAnalysis,
Innovation,
machinelearning
Published by Free artificial intelligence and machine learning video tutorial resource
Related video tutorials
Subscribe to:
Post Comments (Atom)
Most watched
-
Quick answer: Qwen 3 32B needs about 22.7 GB of VRAM at Q4_K_M with a 4k-token context — a used 24 GB card like the RTX 3090 is the minimu...
-
Using GPUs in TensorFlow, TensorBoard in notebooks, finding new datasets, & more! (#AskTensorFlow) [Collection] In a special live ep...
-
Inside TensorFlow: Summaries and TensorBoard [Collection] Take an inside look into the TensorFlow team’s own internal training sessions-...
-
Quick answer: DeepSeek V4 Flash is a Mixture-of-Experts model: all 284 billion parameters must be stored, so combined RAM plus VRAM — not ...
-
Quick answer: For an 8B model at Q4, local electricity costs roughly $0.05 per million output tokens — against about $0.53 per million ble...
-
Hey everyone This is Ujjwal kapil B.tech 2nd year Ai and Ml In this video we covered one of the most important question "COLLEGE KAB O...
-
Quick answer: For machine-learning work, buy an external SSD over a spinning hard drive, pick USB 3.2 Gen 2 (10 Gbps) or faster, and size f...
-
Quick answer: Yes, for most budget builders: a used RTX 3090 is still the cheapest route to 24 GB of VRAM with full CUDA support. Capacity...
-
This video is a crash course on understanding how finetuning on LLM models can be performed uing QLORA,LORA, Quantization using LLama2, Grad...
-
In this video, you'll learn how to use machine learning, computer vision and deep learning to create a football analysis system. This pr...
No comments:
Post a Comment