Convolutional Neural Networks §

Group: 4 #group-4

Relations §

• Deep Learning: Convolutional neural networks are a type of deep neural network commonly used for processing grid-like data such as images.
• Deep Learning: Convolutional Neural Networks are a type of Deep Learning model
• Backpropagation: Backpropagation is used to train Convolutional Neural Networks by adjusting weights
• Feature Extraction: Convolutional Neural Networks automatically learn to extract relevant features from input data
• Data Augmentation: Data Augmentation is a technique used to increase the size and diversity of training data
• Computer Vision: Convolutional Neural Networks are a type of deep learning model widely used in Computer Vision for tasks like image classification and object detection.
• Convolution Operation: The convolution operation is a key component of Convolutional Neural Networks
• Computer Vision: Convolutional Neural Networks are widely used in Computer Vision tasks
• Pooling Operation: Pooling operations are used in Convolutional Neural Networks to reduce spatial dimensions
• Image Recognition: Convolutional Neural Networks are particularly effective for Image Recognition tasks
• Transfer Learning: Transfer Learning can be used to leverage pre-trained Convolutional Neural Networks for new tasks
• Convolutional Filters: Convolutional Filters are learned during training to detect relevant features
• Architectures (VGGNet, ResNet, etc.): Various architectures like VGGNet and ResNet have been proposed for Convolutional Neural Networks
• Stride: Stride determines the step size of the convolution operation
• Neural Networks: Convolutional neural networks are a type of neural network particularly well-suited for processing grid-like data such as images.
• Regularization: Regularization techniques like dropout are used to prevent overfitting in Convolutional Neural Networks
• Fully Connected Layers: Fully Connected Layers are often used at the end of Convolutional Neural Networks for classification
• Activation Functions: Activation functions like ReLU are used in Convolutional Neural Networks to introduce non-linearity
• Receptive Fields: Receptive Fields determine the region of input data that each neuron is connected to
• Gradient Descent: Gradient Descent optimization algorithms are used to update weights during training
• Padding: Padding is used to control the spatial dimensions of the output after convolution
• Overfitting: Overfitting is a common issue in Convolutional Neural Networks that needs to be addressed