The video tutorial shows how to use Python and the OpenAI API to generate images from a chat. The steps include installing Python, choosing a coding environment, installing required libraries using pip, creating an API key by registering on the OpenAI website, and writing Python code in Visual Studio Code. The tutorial demonstrates generating different types of images using the API, specifying image types, and improving image quality. It is noted that the results may vary for the free version of the API.
Complete Version:
https://www.youtube.com/watch?v=jF5nEuePlqE

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#Python #OpenAI #API #imagegeneration #visualstudiocode #Pillow #requests #chatbot #imageprocessing #computergraphics #artificialintelligence #machinelearning #tutorial #imagequality #imageoutput #programming #Python #API #OpenAI #image_generation #Visual_Studio_Code #Pillow #requests #programming #AI #machine_learning #computer_vision #deep_learning #natural_language_processing #chatbot #image_quality #tutorial

⚜️Neural network course session three::
3️⃣An Illustrative Example

🔵In this MATLAB tutorial, learn how to implement Principal Component Analysis (PCA) and Anchor Graphs for dimensionality reduction. The video covers the core concepts, provides step-by-step code explanations, and demonstrates how to visualize and compare results. By the end of this tutorial, you'll be able to apply PCA and Anchor Graphs to your own datasets in MATLAB. Suitable for both beginners and experienced users.

✅Visualizing PCA results in MATLAB
✅Introduction to Anchor Graphs and their advantages
✅Constructing Anchor Graphs in MATLAB
✅Using Anchor Graphs for efficient dimensionality reduction
✅Comparing PCA and Anchor Graph results
🔻YouTube: third session

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#MATLAB #PCA #PrincipalComponentAnalysis #AnchorGraphs #DimensionalityReduction #MachineLearning #DataScience #Tutorial #Eigenvectors #Covariance #DataVisualization #Code #Programming

⚜️Neural network course session four::
4️⃣Perceptron Learning Rule

🔵In this MATLAB tutorial video, we dive into the fundamentals of the Perceptron Learning Rule, a powerful algorithm for training single-layer neural networks. Through practical examples and step-by-step explanations, you'll learn how to implement the Perceptron Learning Rule in MATLAB to solve linearly separable classification problems.
We cover key concepts such as:

✅Perceptron architecture and decision boundaries
✅Supervised learning and training sets
✅Weight and bias updates using the Perceptron Learning Rule
✅Convergence and limitations of the Perceptron network
🔻YouTube: third session

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🎬✨ Title:
🚀 How to Run Local AI Models with MATLAB GUI 🖥🤖

📝📌 Description:
Dive into 🌟 local AI using OLLAMA 🦙! Learn to download and run powerful open-source models (DeepSeek-R1 1.5B, Qwen 0.5B) locally and integrate them into an interactive MATLAB GUI chatbot 🛠🎨.

🚀🌟 You'll Learn:
- ⚙️ Install OLLAMA quickly 💻✨
- 📥 Easily download DeepSeek-R1 and Qwen 📂
- 🎨 Build a user-friendly chatbot in MATLAB 🤖💬
- 🧠 Test AI logical reasoning:
- ✅ Logical inference: Apples 🍎 and fruits 🍓
- ✅ Comparative reasoning: Which is smallest? 📏
- 📊 Compare with online models (Claude.ai, ChatGPT, DeepSeek R1)

👥👩‍💻 For:
- AI enthusiasts exploring private AI solutions 🔐
- Researchers integrating AI and MATLAB 👨‍💻
- Students & academics in NLP experiments 🎓📚

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#OLLAMA #MATLAB #LocalAI #Chatbot #AIIntegration #MachineLearning #Tutorial