DeepSeek R1 vs Google Gemini [Comparison] Ollama FAISS VectorDB RAG Streamlit GenAI Project Tutorial

Are you curious about how to run DeepSeek R1 locally using Ollama and compare it with Google Gemini Pro? In this video, we walk through setting up DeepSeek R1 on Ollama, building a Streamlit application, and testing the reasoning capabilities of both LLMs in RAG-based and non-RAG scenarios!

What You'll Learn in This Video?
1. How to install and run DeepSeek R1 on Ollama locally
2. How DeepSeek R1 compares to Google Gemini Pro 1.5
3. Building a Streamlit AI app with and without RAG
4. Testing logical reasoning and problem-solving capabilities
5. Comparing responses from both models with real-world prompts

Quick Breakdown:
1. Introduction to DeepSeek R1 & Gemini Pro 1.5
2. What is DeepSeek R1? (Chain of Thought + GRPO Trainer)
3. What is Google Gemini Pro? (Mixture of Experts AI)
4. Running DeepSeek R1 locally with Ollama (Installation & Setup)
5. Building a Streamlit AI App with DeepSeek R1 & Gemini Pro 1.5
6. Running RAG-based AI Responses for better context precision
7. Evaluating reasoning & logical capabilities of both models
8. Conclusion: Which LLM performs better?

Example Prompts Used in the Video:
DeepSeek R1: "If all humans are mortal and Socrates is a human, what can we conclude about Socrates?"
Expected Output: "Socrates is mortal."

Google Gemini Pro 1.5: "All dogs are mammals. Fido is a dog. All mammals have lungs. Therefore..."
Expected Output: "Therefore, Fido has lungs."

Why Watch This?
Best tutorial on running DeepSeek R1 locally on Ollama
Hands-on coding walkthrough for a complete Streamlit AI app
Real-time performance comparison between DeepSeek R1 & Gemini Pro
Learn how RAG-based AI improves context precision

Source Code: https://github.com/simranjeet97/DeepS...

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Playlists that make you skilled up
1. GenAI Full Course with LLM Fine Tuning and Evaluation: https://bit.ly/4bJwZla
2. Learn RAG from scratch with GenAI projects: https://bit.ly/3Zl47KD
3. Latest AI/GenAI Research Papers Explained: https://bit.ly/4huqEMT
4. RAG and LLM Use Cases in Finance Domain Projects: https://bit.ly/3AGSRQm
4. Prompt Engineering: https://bit.ly/42v376M
5. Financial Data Analysis and Financial Modelling: https://bit.ly/3OCWI5O
6. Artificial Intelligence Projects: https://bit.ly/3L8lhEi
7. Predict IPL 2023 Winner (End to End Data Science Project): https://bit.ly/3BfC3N9
8. Explainable AI (XAI) Machine Learning: https://bit.ly/3gsuIxb
9. Face Recognition: https://bit.ly/2YphpHm

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