MLJourney
Day 19
Week 3

CNN for Images: Understanding Convolutional Neural Networks

Overview

Convolutional Neural Networks (CNNs) are the backbone of modern computer vision systems. They're designed to automatically and adaptively learn spatial hierarchies of features from images.

Today, you'll understand the architecture of CNNs including convolutional, pooling, and fully connected layers. You'll implement a CNN using Keras to classify images from the CIFAR-10 dataset.

Key Concepts
  • Convolutional Layers and Feature Maps
  • Max Pooling and Dimensionality Reduction
  • Flattening and Fully Connected Layers
  • Activation Functions (ReLU, Softmax)
  • Building CNNs in Keras
Practice Exercise

Exercise: Image Classification with CNN (CIFAR-10)

  1. Load the CIFAR-10 dataset using tensorflow.keras.datasets.
  2. Preprocess the images (normalize pixel values and one-hot encode labels).
  3. Build a CNN using the Sequential API with Conv2D and MaxPooling2D layers.
  4. Add dropout layers to reduce overfitting.
  5. Compile and train the model using the adam optimizer and categorical_crossentropy.
  6. Evaluate the model and visualize sample predictions and accuracy metrics.
Resources

Deep Learning with CNNs in Keras

Main resource for today

CNNs Explained Visually

Great visual explanation of how convolution and pooling work

Keras Conv2D Documentation

Official guide to Conv2D layer usage

CNN with Keras for Beginners

Step-by-step CNN example using Keras and CIFAR-10

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