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v. t. e. Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). [2] [3] It is a framework with wide support for deep learning algorithms. [4] Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive ...
Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised. [2]
Another class of model-free deep reinforcement learning algorithms rely on dynamic programming, inspired by temporal difference learning and Q-learning. In discrete action spaces, these algorithms usually learn a neural network Q-function Q ( s , a ) {\displaystyle Q(s,a)} that estimates the future returns taking action a {\displaystyle a} from ...
A deep Q-network (DQN) is a type of deep learning model that combines a deep neural network with Q-learning, a form of reinforcement learning. Unlike earlier reinforcement learning agents, DQNs that utilize CNNs can learn directly from high-dimensional sensory inputs via reinforcement learning.
Machine learningand data mining. In machine learning, a deep belief network ( DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. [1]
Deep Learning Studio is a software tool that aims to simplify the creation of deep learning models used in artificial intelligence. It is compatible with a number of open-source programming frameworks popularly used in artificial neural networks , including MXNet and Google's TensorFlow .
Transfer learning ( TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. [1] For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.
In the 2010s, representation learning and deep neural network-style (featuring many hidden layers) machine learning methods became widespread in natural language processing. That popularity was due partly to a flurry of results showing that such techniques [11] [12] can achieve state-of-the-art results in many natural language tasks, e.g., in ...
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