Convolutional Neural Networks Explained Simply

What You'll Accomplish

• Understand the basic principles of Convolutional Neural Networks (CNNs).
• Explain how CNNs differ from Artificial Neural Networks (ANNs) for image recognition.
• Identify the core components of a CNN: convolution, ReLU, pooling, and fully connected layers.
• Describe the concept of feature extraction and how it is implemented in CNNs.
• Discuss the benefits of CNNs, including connection sparity, location invariance, and parameter sharing.
• Explain the purpose of ReLU activation and pooling layers in CNNs.
• Describe data augmentation techniques for improving the robustness of CNNs.
• Summarize the end-to-end process of using CNNs for image classification.