Machine Learning: Convolutional Neural Networks and Other Architectures

Flashcards covering key concepts from the lecture on Machine Learning, focusing on Convolutional Neural Networks and various architectural types.

Convolutional Neural Networks (CNNs)

A type of neural network that utilizes spatially local connections and replicated patterns of weights across units.

Image Classification

The process of taking an image as input and outputting what is depicted in the image.

Viewpoint Variation

A challenge in image classification where the same object may appear differently based on its orientation relative to the camera.

Deformation

A challenge where many objects may be presented in various configurations, affecting recognition accuracy.

Occlusion

A situation when objects are partially hidden behind other objects, complicating their identification.

Pooling

A technique in CNNs that summarizes and condenses a region of feature maps, typically through operations like max-pooling or average-pooling.

Recurrent Neural Networks (RNNs)

A type of neural network designed to process sequences of data, allowing cycles in computation to account for temporal dependencies.

Long Short-Term Memory (LSTM)

A specialized form of RNN that includes gating mechanisms to maintain long-term memory over time.

Autoencoders

An unsupervised artificial neural network architecture used to learn efficient representations of data, consisting of an encoder and a decoder.

Generative Adversarial Networks (GANs)

A architecture comprising two neural networks, the generator and the discriminator, that compete against each other to improve the quality of generated outputs.