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+# 📜 AND Gate Implementation Using a Simple Neural Network
+
+
+
+
+
+## 🎯 AIM
+To develop a simple neural network that mimics the behavior of an AND gate using a perceptron model.
+
+
+## 📊 DATASET LINK
+NOT USED
+
+
+## 📓 KAGGLE NOTEBOOK
+
+[https://www.kaggle.com/code/thatarguy/and-gate-using-simple-neural-network](https://www.kaggle.com/code/thatarguy/and-gate-using-simple-neural-network)
+
+??? Abstract "Kaggle Notebook"
+
+
+
+## ⚙️ TECH STACK
+
+| **Category** | **Technologies** |
+|--------------------------|---------------------------------------------|
+| **Languages** | Python |
+| **Libraries/Frameworks** | TensorFlow, Keras, Numpy, Matplotlib |
+| **Tools** | Kaggle, Jupyter, |
+
+---
+
+## 📝 DESCRIPTION
+!!! info "What is the requirement of the project?"
+ - Develop a minimal neural network that performs binary classification.
+ - Train a perceptron to correctly compute the AND logic operation.
+
+
+??? info "How is it beneficial and used?"
+ - Demonstrates how logic gates can be implemented using machine learning.
+ - Forms a foundation for more complex deep learning projects.
+
+??? info "How did you start approaching this project? (Initial thoughts and planning)"
+ - Explored TensorFlow/Keras for implementing simple models.
+ - Designed a neural network with a single-layer perceptron.
+
+??? info "Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.)."
+ - TensorFlow and Keras documentation.
+
+
+---
+
+## 🔍 PROJECT EXPLANATION
+
+### 🛤 PROJECT WORKFLOW
+
+
+!!! success "Project workflow"
+
+ ``` mermaid
+ graph LR
+ A[Start] --> C[Create Perceptron Model]
+ C --> D[Train the Model]
+ D --> E[Evaluate Performance]
+ E --> F[End]
+ ```
+
+
+=== "Step 1"
+ - Define the input-output pairs for the AND gate.
+
+=== "Step 2"
+ - Implement a single-layer perceptron model.
+
+=== "Step 3"
+ - Train the model using supervised learning with gradient descent.
+
+=== "Step 4"
+ - Evaluate the model's accuracy in predicting AND gate outputs.
+
+=== "Step 5"
+ - Test the trained model with different inputs.
+
+
+
+---
+
+### 🖥 CODE EXPLANATION
+=== "Perceptron Model"
+ - Implemented using NumPy and TensorFlow/Keras.
+ - Uses a single-layer neural network with sigmoid activation.
+
+
+---
+
+### ⚖️ PROJECT TRADE-OFFS AND SOLUTIONS
+=== "Trade Off 1"
+- Accuracy vs. Simplicity: A single-layer perceptron is sufficient for the AND gate but cannot handle more complex problems like XOR.
+- Solution: Use multi-layer perceptrons for non-linearly separable problems.
+---
+
+## 🖼 SCREENSHOTS
+
+!!! example "Model performance graphs"
+
+ === "Training Progress"
+ 
+
+---
+
+## 📉 MODELS USED AND THEIR EVALUATION METRICS
+
+
+| Model | Accuracy |
+|------------|----------|
+| Perceptron | 100% |
+
+---
+
+## ✅ CONCLUSION
+
+### 🔑 KEY LEARNINGS
+!!! tip "Insights gained from the data"
+ - Understanding of perceptrons and logic gates.- Implementing simple neural networks.
+
+??? tip "Improvements in understanding machine learning concepts"
+ - Learned about activation functions and training neural networks.
+
+---
+
+### 🌍 USE CASES
+
+=== "Simple AI Applications"
+ - Basic logic implementations in AI systems.
+
+=== "Introduction to Neural Networks"
+ - A stepping stone to more advanced ML applications.
diff --git a/docs/projects/deep-learning/index.md b/docs/projects/deep-learning/index.md
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+