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# Set the page configuration
st.set_page_config(page_title="ML vs DL", page_icon="🤖")
# Custom CSS for styling
st.markdown("""
<style>
body {
font-family: 'Arial', sans-serif;
background-color: #f4f4f4;
}
.title {
text-align: center;
font-size: 2.5rem;
color: black;
margin-bottom: 10px;
}
.subtitle {
text-align: center;
font-size: 1.2rem;
color: violet;
margin-bottom: 30px;
}
.table-container {
margin: 0 auto;
width: 80%;
background-color: #fff;
border-radius: 10px;
box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.1);
padding: 20px;
}
.styled-table {
width: 100%;
border-collapse: collapse;
font-size: 1rem;
}
.styled-table thead tr {
background-color: #4CAF50;
color: #ffffff;
text-align: left;
}
.styled-table th, .styled-table td {
padding: 12px 15px;
}
.styled-table tbody tr {
border-bottom: 1px solid #dddddd;
}
.styled-table tbody tr:nth-of-type(even) {
background-color: #f3f3f3;
}
.styled-table tbody tr:last-of-type {
border-bottom: 2px solid #4CAF50;
}
</style>
""", unsafe_allow_html=True)
# Title and subtitle
st.markdown('<div class="title">Machine Learning vs Deep Learning</div>', unsafe_allow_html=True)
st.markdown('<div class="subtitle">Understanding the key differences</div>', unsafe_allow_html=True)
# HTML table for differences
html_table = """
<div class="table-container">
<table class="styled-table">
<thead>
<tr>
<th>Aspect</th>
<th>Machine Learning</th>
<th>Deep Learning</th>
</tr>
</thead>
<tbody>
<tr>
<td>Definition</td>
<td>Machine Learning is a tool which needs statistical concepts to copy / mimic the learning ability in natural intelligence </td>
<td>Deep Learning is a tool which needs logical structure known as neural network to copy / mimic the learning ability in natural intelligence</td>
</tr>
<tr>
<td>Data Dependency</td>
<td>ML performs well with structured data (**tabular data**) and smaller datasets</td>
<td>DL is hungry of data as it requires large amounts of unstructured data and also structured data to perform well</td>
</tr>
<tr>
<td>Performance</td>
<td>ML have treshold as the data increases the performance becomes stable</td>
<td>DL performance increases as the data increases because DL is hungry of data</td>
</tr>
<tr>
<td>Memory Management</td>
<td>ML memory uasage is less as it uses less data</td>
<td>DL memory usage is large as it has huge data</td>
</tr>
<tr>
<td>Hardware Requirements</td>
<td>ML works on standard CPUs; lower hardware demands</td>
<td>DL requires GPUs for efficient computation.</td>
</tr>
<tr>
<td>Interpretability</td>
<td>ML is more interpretable as it works on smaller datasets </td>
<td>DL is less interpretable as it works on complex neural networks</td>
</tr>
<tr>
<td>Training Time</td>
<td>ML is relatively faster to train models as it uses less data</td>
<td>DL training can take significantly longer</td>
</tr>
</tbody>
</table>
</div>
"""
# Render the HTML table
st.markdown(html_table, unsafe_allow_html=True)
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