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Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Bahasa Indonesia; עברית ... Pages in category "Deep learning" The following 48 pages are in this category, out of 48 total. This list may not reflect recent ...
MATLAB + Deep Learning Toolbox (formally Neural Network Toolbox) MathWorks: 1992 Proprietary: No Linux, macOS, Windows: C, C++, Java, MATLAB: MATLAB: No No Train with Parallel Computing Toolbox and generate CUDA code with GPU Coder [23] No Yes [24] Yes [25] [26] Yes [25] Yes [25] Yes With Parallel Computing Toolbox [27] Yes Microsoft Cognitive ...
Deep learning spurs huge advances in vision and text processing. 2020s Generative AI leads to revolutionary models, creating a proliferation of foundation models both proprietary and open source, notably enabling products such as ChatGPT (text-based) and Stable Diffusion (image based). Machine learning and AI enter the wider public consciousness.
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]
[25] [26] 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 ...
Double Q-learning [23] is an off-policy reinforcement learning algorithm, where a different policy is used for value evaluation than what is used to select the next action. In practice, two separate value functions Q A {\displaystyle Q^{A}} and Q B {\displaystyle Q^{B}} are trained in a mutually symmetric fashion using separate experiences.
PyTorch is a machine learning library based on the Torch library, [4] [5] [6] used for applications such as computer vision and natural language processing, [7] originally developed by Meta AI and now part of the Linux Foundation umbrella.