Update pages/Difference_between_ML&DL.py

pages/Difference_between_ML&DL.py

@@ -1,2 +1,15 @@
 import streamlit as st
-st.markdown("<h1 style='text-align: center; color: Balck;'>Difference between ML & DL</h1>", unsafe_allow_html=True)
+st.markdown("<h1 style='text-align: center; color: Balck;'>Difference between ML & DL</h1>", unsafe_allow_html=True)
+table = """
+| **Aspect**               | **Machine Learning**                              | **Deep Learning**                              |
+|---------------------------|--------------------------------------------------|-----------------------------------------------|
+| **Definition**            | Subset of AI focused on learning patterns in data. | Subset of ML using neural networks with multiple layers. |
+| **Data Dependency**       | Works well with smaller datasets.                 | Requires large datasets to perform well.      |
+| **Feature Engineering**   | Manual feature engineering is often required.     | Automatically extracts features from raw data. |
+| **Model Interpretability**| Models are more interpretable and easier to debug.| Models are often considered "black boxes."    |
+| **Hardware**              | Runs efficiently on CPUs.                         | Requires GPUs or TPUs for faster computation. |
+| **Complexity**            | Suitable for simpler tasks like linear predictions. | Suitable for complex tasks like image or speech recognition. |
+| **Training Time**         | Training time is generally shorter.               | Training time can be very long.               |
+| **Examples**              | Spam detection, fraud detection, basic predictions.| Image recognition, autonomous vehicles, NLP.  |
+"""
+st.markdown(table)