import streamlit as st
# Dynamic CSS
custom_css = """
"""
# Inject CSS into Streamlit app
st.markdown(custom_css, unsafe_allow_html=True)
# Header Section
st.markdown("DIFFERENCES BETWEEN ML AND DL
", unsafe_allow_html=True)
# Content for Machine Learning
st.markdown(
"""
Key Points in Machine Learning
- Data Points: Machine Learning models can be trained on smaller datasets.
- Hardware for Training: Training can be done on standard CPUs.
- Training Time: Requires less time due to smaller dataset sizes and simpler algorithms.
- Algorithm Complexity: Includes simpler models like linear regression and more complex ones like decision trees or random forests.
- Analysis Complexity: Involves identifying patterns and relationships in data.
- Application Areas: Widely used for tasks such as regression, classification, and clustering.
""",
unsafe_allow_html=True
)
# Content for Deep Learning
st.markdown(
"""
Key Points in Deep Leraning
- Data Points: Machine Learning models csan be trained on larger dataset.
- Hardware for Training: Training can be done on GPUs.
- Training Time: Requires more time due to larger dataset sizes and bigger algorithms.
- Algorithm Complexity: Deep learning algorithms are based on artificial neural networks that consist of multiple layers and nodes..
- Analysis Complexity:Uses complex neural networks with multiple layers to analyze more intricate patterns and relationships.
- Application Areas: Deep learning is mostly used for complex tasks such as image and speech recognition, natural language processing, and autonomous systems.
""",
unsafe_allow_html=True
)