Used primarily for image recognition and classification tasks. Comprised of convolutional layers applying filters to input data. Enable learning of spatial hierarchies and patterns within images.
Designed to process sequential data by retaining information from previous inputs. Widely applied in natural language processing, time series analysis, and speech recognition tasks.
Specialized RNNs capable of learning long-term dependencies in sequential data. Address the vanishing gradient problem and essential for memory retention tasks like language modeling and sentiment analysis.
Probabilistic graphical models consisting of multiple layers of restricted Boltzmann machines. Applied in unsupervised learning tasks such as dimensionality reduction, feature learning, and collaborative filtering.
– Neural networks encoding input data into a compact representation and reconstructing it. – Utilized for tasks including data compression, denoising, and anomaly detection.
Fill in some textCombine deep learning with reinforcement learning for learning complex decision-making policies. Successful in tasks like video game playing, robotics control, and autonomous navigation.
Architectures based on self-attention mechanisms, prevalent in natural language processing. Parallelizable and capable of capturing global dependencies, advancing machine translation, text summarization, and language understanding.
Integrate neural networks with external memory units for learning algorithmic tasks and reasoning. Suitable for tasks like program synthesis and symbolic reasoning due to their capability to manipulate complex data structures.