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