Welcome to our in-depth session on Convolutional Neural Networks (CNNs)!
In this session, we delve into the fundamentals of CNNs, their architecture, and practical applications in deep learning. This session is crucial for anyone looking to understand and implement CNNs for image processing and computer vision tasks.
🔍 In this session, we will cover:
- Introduction to CNNs: Understand what Convolutional Neural Networks are, their importance, and where they are used.
- CNN Architecture: Learn about the key components of a CNN, including:
Convolutional Layers
- Forward and Backward Propagation: Understanding how data flows through the network and how the model learns through backpropagation.
- Regularization Techniques: Techniques to prevent overfitting, such as dropout, data augmentation, and batch normalization.
- Popular CNN Architectures: Overview of famous CNN architectures like LeNet, AlexNet, VGGNet, GoogLeNet, ResNet, and MobileNet.
- Implementing CNNs in Python: Step-by-step guide to building and training CNNs using popular deep learning frameworks such as TensorFlow and PyTorch.
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