Introduction to Deep Learning (Weeks 12)
Deep Learning represents a significant evolution in machine learning, particularly through the utilization of Convolutional Neural Networks (CNNs) which address the limitations of traditional Artificial Neural Networks (ANNs) when dealing with high-dimensional image data. CNNs employ specialized layers such as convolutional and pooling layers to extract features hierarchically, enhancing computational efficiency and robustness to spatial variations. The module also emphasizes essential techniques like Dropout and Batch Normalization for regularization, and introduces Transfer Learning as an effective approach for leveraging pre-trained models in new tasks.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- CNNs are designed to overcome the limitations of traditional ANNs when processing image data.
- Convolutional layers extract features through filters that capture spatial hierarchies, while pooling layers reduce dimensionality.
- Regularization techniques like Dropout and Batch Normalization are crucial in preventing overfitting in deep learning models.
Key Concepts
- -- Convolutional Neural Network (CNN)
- A specialized deep learning architecture designed to process data with a grid-like topology, such as images, using convolutional layers to automatically learn features.
- -- Dropout
- A regularization technique that randomly sets a certain fraction of neurons to zero during training to prevent overfitting.
- -- Transfer Learning
- A method where a pre-trained model is adapted for a new task, allowing for faster training and improved performance, especially with limited data.
Additional Learning Materials
Supplementary resources to enhance your learning experience.