· Deep Learning · 2 min read
📋 Prerequisites
- Basic Python knowledge
- Curiosity about data and AI
🎯 What You'll Learn
- Understand what regression is and its use cases
- Understand what classification is and its use cases
- Identify the difference between regression and classification tasks
- Build confidence in choosing the right approach for your projects
Introduction
When starting with deep learning, it is essential to understand regression and classification, as most real-world problems fall into one of these categories.
1️⃣ What is Regression?
Regression is used to predict continuous numerical values based on input features.
Examples:
✅ Predicting house prices based on location and size.
✅ Estimating temperature for tomorrow.
✅ Forecasting sales numbers.
In deep learning:
- The output layer usually has one neuron without an activation function (or sometimes a linear activation).
- The loss function often used is Mean Squared Error (MSE), which measures the average squared difference between predicted and actual values.
2️⃣ What is Classification?
Classification is used to predict categorical outcomes (classes) based on input features.
Examples:
✅ Determining if an email is spam or not (binary classification).
✅ Classifying images of animals into categories like cat, dog, or bird (multi-class classification).
✅ Diagnosing diseases based on medical data.
In deep learning:
- The output layer usually has:
- One neuron with sigmoid activation for binary classification.
- Multiple neurons with softmax activation for multi-class classification.
- The loss function often used is Cross-Entropy Loss, which measures how well predicted probabilities align with true labels.
3️⃣ Key Differences Between Regression and Classification
| Aspect | Regression | Classification |
|---|---|---|
| Output | Continuous numerical value | Discrete categories |
| Examples | House prices, temperatures | Spam detection, image labels |
| Activation | None / Linear | Sigmoid / Softmax |
| Loss Function | Mean Squared Error | Cross-Entropy Loss |
4️⃣ Practical Examples in Python
Regression Example
# Predicting house prices
import tensorflow as tf
from tensorflow.keras import layers
model = tf.keras.Sequential([
layers.Dense(64, activation='relu', input_shape=[num_features]),
layers.Dense(1) # Regression output
])
model.compile(optimizer='adam', loss='mse', metrics=['mae'])Classification Example
# Classifying images of cats and dogs
model = tf.keras.Sequential([
layers.Dense(64, activation='relu', input_shape=[num_features]),
layers.Dense(1, activation='sigmoid') # Binary classification output
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])5️⃣ When to Use Regression vs Classification
✅ Use regression when your target variable is a continuous number (price, temperature, etc.).
✅ Use classification when your target variable is a category or label (spam/not spam, dog/cat, etc.).
Understanding this distinction will help you choose the right models and evaluation metrics for your projects.
Conclusion
Regression and classification are the building blocks of predictive modeling in deep learning.
✅ Regression predicts quantitative values.
✅ Classification predicts categorical labels.
Understanding these concepts will prepare you for practical deep learning projects.
What’s Next?
✅ Explore how to prepare datasets for regression and classification.
✅ Learn about evaluation metrics for each (MAE, RMSE, Accuracy, F1-score).
✅ Continue building your knowledge with Beginner Deep Learning Key Concepts.
Join our SuperML Community to discuss your learning journey and get guidance for your projects.
Happy Learning! 🎯