Deep Learning Algorithms - Level 2 Test

RNNs are a class of neural networks designed to recognize patterns in sequences of data, such as time series or natural language. They use their internal state (memory) to process sequences of inputs, making them suitable for tasks like language modeling and speech recognition.

LSTMs are a type of RNN designed to overcome the vanishing gradient problem, enabling them to learn long-term dependencies. They are effective for tasks involving long sequence data, such as language translation and time-series prediction.

Autoencoders are neural networks used for unsupervised learning of efficient codings. They work by encoding input data into a lower-dimensional representation and then reconstructing the output from this representation. They are widely used for dimensionality reduction and anomaly detection.

GANs consist of two neural networks, a generator and a discriminator, that compete against each other. The generator creates fake data, while the discriminator evaluates its authenticity. GANs are known for generating realistic synthetic data, such as images and videos.

Transformers are models that process sequential data by focusing on the relationship between all elements in the sequence simultaneously using self-attention mechanisms. They have revolutionized natural language processing tasks, including translation and text generation.

DRL combines reinforcement learning with deep learning techniques to create systems that can learn to make decisions by interacting with their environment. It's used in applications such as robotics, gaming, and autonomous vehicles.

RBFNs are a type of artificial neural network that uses radial basis functions as activation functions. They are typically used for function approximation, time-series prediction, and classification tasks.

MLPs are feedforward neural networks with multiple layers of neurons. Each layer is fully connected to the next one, and they are used for a variety of tasks including classification, regression, and pattern recognition.

SOMs are unsupervised learning algorithms that produce a low-dimensional representation of high-dimensional data. They are used for visualizing and interpreting complex data patterns, such as clustering and feature mapping.

DBNs are generative neural network models composed of multiple layers of stochastic, latent variables. They are trained in a greedy layer-wise manner and are used for unsupervised learning tasks, such as feature learning and pre-training for deep neural networks.

RBMs are stochastic neural networks that can learn a probability distribution over its set of inputs. They are the building blocks of DBNs and are used for dimensionality reduction, classification, and collaborative filtering.