Update pages/3.Machine learning vs Deep Learning.py

pages/3.Machine learning vs Deep Learning.py

@@ -0,0 +1,17 @@
+import streamlit as st 
+import numpy as np 
+import pandas as pd 
+
+st.header(":red[**Machine learning vs Deep learning**]")
+st.subheader(":blue[**Difference between Machine learning and deep learning**]")
+st.write("Here are the key differences between Machine Learning (ML) and Deep Learning (DL)")
+st.write("""
+1. Definition: ML is a subset of AI that focuses on learning patterns from data, while DL is a subset of ML that uses neural networks to mimic the human brain.
+2. Data Requirement: ML performs well with small to medium datasets, whereas DL requires large datasets for accurate results.
+3. Feature Engineering: In ML, feature selection is done manually, while DL automatically extracts features from raw data.
+4. Model Complexity: ML uses simpler algorithms like regression or decision trees, whereas DL uses complex models with multiple layers (neural networks).
+5. Hardware: ML works on regular computers, but DL requires specialized hardware like GPUs or TPUs for faster processing.
+6. Performance: ML struggles with unstructured data, while DL excels in handling unstructured data like images, videos, and text.
+7. Training Time: ML models train faster, while DL models often require significantly more time to train.
+8. Applications: ML is used for tasks like fraud detection, price predictions, and recommendations. DL is used for advanced tasks like face recognition, self-driving cars, and language translation.
+""")