From 530734384b3d2ee8d41ac8f8594435c38aa67f05 Mon Sep 17 00:00:00 2001
From: that-ar-guy <mohdarahman124@gmail.com>
Date: Fri, 7 Feb 2025 11:04:09 +0530
Subject: [PATCH] added and gate

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 docs/projects/deep-learning/index.md          |   8 +
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 create mode 100644 docs/projects/deep-learning/and-gate-using-neural-network.md

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+# 📜 AND Gate Implementation Using a Simple Neural Network  
+
+<div align="center">
+    <img src="https://www.symbols.com/images/symbol/1/1729_and-gate.png" />
+</div>
+
+## 🎯 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 
+<!-- Attach both links Kaggle URL/ Embed URL public notebook link. -->
+[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"
+
+    <iframe 
+        src="https://www.kaggle.com/code/thatarguy/and-gate-using-simple-neural-network" 
+        height="600" 
+        style="margin: 0 auto; width: 100%; max-width: 950px;" 
+        frameborder="0" 
+        scrolling="auto" 
+        title="cvd-risk-prediction-system">
+    </iframe>
+
+## ⚙️ 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 
+<!-- Draft a visualization graph of your project workflow using mermaid -->
+
+!!! success "Project workflow"
+
+    ``` mermaid
+      graph LR
+        A[Start] --> C[Create Perceptron Model]
+        C --> D[Train the Model]
+        D --> E[Evaluate Performance]
+        E --> F[End]
+    ```
+
+<!-- Clearly define the step-by-step workflow followed in the project. You can add or remove points as necessary. -->
+=== "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"
+    ![img](https://vtupulse.com/wp-content/uploads/2020/12/image-21-1024x372.png)
+
+--- 
+
+## 📉 MODELS USED AND THEIR EVALUATION METRICS 
+<!-- Summarize the models used and their evaluation metrics in a table. -->
+
+|    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
index 7d210a0f..562f86f8 100644
--- a/docs/projects/deep-learning/index.md
+++ b/docs/projects/deep-learning/index.md
@@ -11,6 +11,14 @@
     <p style="font-size: 12px;">📅 2025-01-10 | ⏱️ 10 mins</p>
   </div>
 </a>
+<a href="and-gate-using-neural-network" style="padding: 0 2px 0 16px; background-color: rgba(39, 39, 43, 0.4); border: 1px solid rgba(76, 76, 82, 0.4); border-radius: 10px; box-shadow: 0 4px 8px rgba(0,0,0,0.1); overflow: hidden; transition: transform 0.2s; display: flex; align-items: center;">
+  <img src="https://www.symbols.com/images/symbol/1/1729_and-gate.png" alt="" style="width: 300px; height: 150px; object-fit: cover; border-radius: 10px;" />
+  <div style="padding: 15px;">
+    <h2 style="margin: 0; font-size: 20px;">AND Gate Implementation Using a Simple Neural Network</h2>
+    <p style="font-size: 16px;">Deep learning algorithm for image and video recognition.</p>
+    <p style="font-size: 12px;">📅 2025-02-06 | ⏱️ 10 mins</p>
+  </div>
+</a>
 
 
 </div>