pages/Difference_between_ML&DL.py

import streamlit as st
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)