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  2. Deep learning - Wikipedia

    en.wikipedia.org/wiki/Deep_learning

    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]

  3. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper "Attention Is All You Need". [1] Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table. [1]

  4. History of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/History_of_artificial...

    t. e. Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by neural circuitry. [1] [a] While some of the computational implementations ANNs relate to earlier discoveries in mathematics, the first implementation of ANNs was by psychologist Frank Rosenblatt, who ...

  5. Deep reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Deep_reinforcement_learning

    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 ...

  6. Comparison of deep learning software - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_deep...

    Download as PDF; Printable version ... Deep-learning software by name. Software Creator Initial release Software license Open source Platform Written in

  7. Fine-tuning (deep learning) - Wikipedia

    en.wikipedia.org/wiki/Fine-tuning_(deep_learning)

    In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained 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" (not updated during the backpropagation step). [2]

  8. ImageNet - Wikipedia

    en.wikipedia.org/wiki/ImageNet

    The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3] ImageNet contains more than 20,000 ...

  9. Frank Rosenblatt - Wikipedia

    en.wikipedia.org/wiki/Frank_Rosenblatt

    Chesapeake Bay. Alma mater. Cornell University. Known for. Perceptron. Frank Rosenblatt (July 11, 1928 – July 11, 1971) was an American psychologist notable in the field of artificial intelligence. He is sometimes called the father of deep learning [1] for his pioneering work on artificial neural networks .

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