Image Classification with Convolutional Neural Networks (CNNs)

Prep Time:

2-3 hours

Cook Time:

Ensure your data is handled responsibly, especially when working with personal datasets.

Serves:

16+ years

Level:

Advanced

About the Recipe

Understand the architecture and function of convolutional neural networks.
Gain practical experience in building and training a CNN with real-world data.
Learn to evaluate model performance and understand basic image classification techniques.

Introduction to CNNs and Image Classification:
- Explain the basics of convolutional neural networks (CNNs) and their application in image classification tasks.
- Introduce the chosen dataset (CIFAR-10 or MNIST) and its structure.
Setting Up the Environment:
- Install TensorFlow or PyTorch and necessary Python libraries for deep learning.
- Load and preprocess the dataset for training and testing.
Building the CNN Model:
- Guide students through coding a CNN architecture using TensorFlow or PyTorch.
- Define layers including convolutional layers, pooling layers, and fully connected layers.
Training the Model:
- Train the CNN model using the labeled dataset.
- Explain the concept of loss function, optimizer, and metrics used for model evaluation.
Testing and Evaluation:
- Evaluate the trained model's performance on the test dataset.
- Discuss metrics such as accuracy, precision, and recall for model assessment.
Fine-tuning and Optimization:
- Explore techniques to improve model performance, such as adjusting hyperparameters or adding regularization.
- Discuss overfitting and methods to mitigate it.
Application and Discussion:
- Discuss real-world applications of image classification and CNNs in areas like healthcare, autonomous vehicles, and security.
- Encourage students to brainstorm and propose their own image classification projects.