Spaces:
Sleeping
Sleeping
File size: 2,502 Bytes
3c5281e 9efab78 3c5281e 9efab78 f14a6d8 9efab78 a16cef5 9a4562b e4963dc 9efab78 975e57d 9efab78 e4963dc 9efab78 46ffc42 e4963dc 9efab78 46ffc42 9efab78 e4963dc 9efab78 9a4562b e4963dc 9efab78 46ffc42 9efab78 e4963dc 1f0896e 9a4562b d4094f5 e74b1af d4094f5 e4963dc dcc41c4 9a4562b 3c5281e 9efab78 46ffc42 9efab78 3ac1506 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
import streamlit as st
# CSS style for the table
css_style = """
<style>
table {
width: 100%;
border-collapse: collapse;
border: 1px solid black;
}
th {
background-color: #f2f2f2;
border: 1px solid black;
padding: 10px;
text-align: center;
}
td {
border: 1px solid black;
padding: 10px;
}
tr:nth-child(even) {
background-color: #f9f9f9;
}
tr:nth-child(odd) {
background-color: #ffffff;
}
</style>
"""
# HTML code for the differences table
html_code = """
<table>
<tr>
<th>S.no</th>
<th>Aspect</th>
<th>Machine Learning (ML)📝💻</th>
<th>Deep Learning (DL)📝💻</th>
</tr>
<tr>
<td>1</td>
<td>Definition</td>
<td>A subset of AI focused on enabling systems to learn from data.</td>
<td>A subset of ML that uses neural networks to process data.</td>
</tr>
<tr>
<td>2</td>
<td>Data Dependency</td>
<td>Performs well on small to medium-sized datasets.</td>
<td>Requires large datasets to perform effectively.</td>
</tr>
<tr>
<td>3</td>
<td>Model Complexity</td>
<td>Uses simple algorithms like linear regression or decision trees.</td>
<td>Utilizes complex architectures like CNNs and RNNs.</td>
</tr>
<tr>
<td>4</td>
<td>Computation Power</td>
<td>Less computationally intensive.</td>
<td>Highly computationally intensive, often requires GPUs.</td>
</tr>
<tr>
<td>5</td>
<td>Feature Engineering</td>
<td>Feature engineering is essential for performance.</td>
<td>Automatically learns features from data.</td>
</tr>
<tr>
<td>6</td>
<td>Applications</td>
<td>Fraud detection, recommendation systems, etc.</td>
<td>Image recognition, natural language processing, etc.</td>
</tr>
<tr>
<td>7</td>
<td> Training Time taken</td>
<td>Typically faster to train due to simpler algorithms</td>
<td> Takes longer to train due to the complexity of models and data size.</td>
</tr>
<tr>
<td>8</td>
<td> Interpretability</td>
<td> Easier to interpret and debug.</td>
<td> Acts as a "black box," making it harder to interpret results.</td>
</tr>
</table>
"""
# Inject CSS into Streamlit
st.markdown(css_style, unsafe_allow_html=True)
# Render the HTML in Streamlit
st.markdown(html_code, unsafe_allow_html=True)
|