import streamlit as st # Title for the app st.markdown("

Machine Learning vs. Deep Learning

", unsafe_allow_html=True) # Table comparison with examples st.markdown("""

Feature	Machine Learning (ML)	Deep Learning (DL)
Learning Ability Mimic	Mimics human learning using statistical concepts.	Mimics human learning using artificial neurons (logical structure).
Data Requirement	Requires less data to perform effectively.	Requires large amounts of data to perform well.
Performance	Works well with structured data.	Works well with both structured and unstructured data.
Data Handling	Works mainly on structured data, unstructured data needs conversion (with potential data loss).	Can directly handle both structured and unstructured data without significant loss.
Memory Usage	Uses less memory.	Uses more memory due to complex models.
Training Time	Generally has less training time.	Requires more time for training due to complex models.
Hardware Requirement	Can run on CPUs with lower storage needs.	Runs better on GPUs with more storage and computational power.
Story Example	Teacher Predicting Student Performance: A teacher uses past exam results to predict student performance, analyzing patterns in study habits and class participation. This is similar to ML, where the model learns from structured data to make predictions and requires less computational power.	Self-Driving Car Learning: A self-driving car processes huge amounts of data from cameras and sensors to understand its environment, learning over time to navigate obstacles. This requires large datasets and more computational power (via GPUs) and longer training times, similar to how DL works.

""", unsafe_allow_html=True) st.image("https://huggingface.co/spaces/hari3485/DiveIntoML/resolve/main/data.jpg") st.markdown(""" This Graph explains the relationship between the accuracy of *Machine Learning (ML) techniques* and *Deep Learning (DL) models* with respect to the size of data: - *ML techniques* perform well on smaller datasets but plateau as data size increases. - *DL models* start with lower accuracy on small datasets but improve significantly as data size grows, outperforming ML techniques on larger datasets. """)