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import streamlit as st
# Dynamic CSS
custom_css = """
<style>
html, body, [data-testid="stAppViewContainer"] {
background: linear-gradient(
rgba(0, 0, 0, 0.6),
rgba(0, 0, 0, 0.6)
),
url("https://cdn.pixabay.com/photo/2023/11/23/17/47/sunset-7704533_1280.jpg") no-repeat center center fixed;
background-size: cover;
font-family: Arial, sans-serif;
color: #ffffff;
}
h1 {
color: #ffffff;
text-align: center;
font-size: 2rem;
margin-top: 2px;
text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.7);
}
.division {
margin: 20px auto;
padding: 20px;
background: rgba(255, 255, 255, 0.1);
border-radius: 10px;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
.division h2 {
color: #ffffff;
margin-bottom: 10px;
font-size: 2rem;
text-shadow: 1px 1px 3px rgba(0, 0, 0, 0.7);
}
.division ul li {
font-size: 1.2rem;
line-height: 1.7;
margin-bottom: 8px;
}
</style>
"""
# Inject CSS into Streamlit app
st.markdown(custom_css, unsafe_allow_html=True)
# Header Section
st.markdown("<h1>DIFFERENCES BETWEEN ML AND DL</h1>", unsafe_allow_html=True)
# Content for Machine Learning
st.markdown(
"""
<div class="division">
<h2>Key Points in Machine Learning</h2>
<ul>
<li><strong>Data Points:</strong> Machine Learning models can be trained on smaller datasets.</li>
<li><strong>Hardware for Training:</strong> Training can be done on standard CPUs.</li>
<li><strong>Training Time:</strong> Requires less time due to smaller dataset sizes and simpler algorithms.</li>
<li><strong>Algorithm Complexity:</strong> Includes simpler models like linear regression and more complex ones like decision trees or random forests.</li>
<li><strong>Analysis Complexity:</strong> Involves identifying patterns and relationships in data.</li>
<li><strong>Application Areas:</strong> Widely used for tasks such as regression, classification, and clustering.</li>
</ul>
</div>
""",
unsafe_allow_html=True
)
# Content for Deep Learning
st.markdown(
"""
<div class="division">
<h2>Key Points in Deep Leraning</h2>
<ul>
<li><strong>Data Points:</strong> Machine Learning models csan be trained on larger dataset.</li>
<li><strong>Hardware for Training:</strong> Training can be done on GPUs.</li>
<li><strong>Training Time:</strong> Requires more time due to larger dataset sizes and bigger algorithms.</li>
<li><strong>Algorithm Complexity:</strong> Deep learning algorithms are based on artificial neural networks that consist of multiple layers and nodes..</li>
<li><strong>Analysis Complexity:</strong>Uses complex neural networks with multiple layers to analyze more intricate patterns and relationships.</li>
<li><strong>Application Areas:</strong> Deep learning is mostly used for complex tasks such as image and speech recognition, natural language processing, and autonomous systems.</li>
</ul>
</div>
""",
unsafe_allow_html=True
)
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