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  2. Deep learning - Wikipedia

    en.wikipedia.org/wiki/Deep_learning

    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.

  3. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]

  4. Outline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Outline_of_machine_learning

    Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]

  5. Differentiable programming - Wikipedia

    en.wikipedia.org/wiki/Differentiable_programming

    Differentiable programming has found use in a wide variety of areas, particularly scientific computing and machine learning. [5] One of the early proposals to adopt such a framework in a systematic fashion to improve upon learning algorithms was made by the Advanced Concepts Team at the European Space Agency in early 2016. [6]

  6. Neural network (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Neural_network_(machine...

    Furthermore, researchers involved in exploring learning algorithms for neural networks are gradually uncovering generic principles that allow a learning machine to be successful. For example, Bengio and LeCun (2007) wrote an article regarding local vs non-local learning, as well as shallow vs deep architecture. [231]

  7. Transformer (deep learning architecture) - Wikipedia

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

    The plain transformer architecture had difficulty converging. In the original paper [1] the authors recommended using learning rate warmup. That is, the learning rate should linearly scale up from 0 to maximal value for the first part of the training (usually recommended to be 2% of the total number of training steps), before decaying again.

  8. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    Google JAX is a machine learning framework for transforming numerical functions. [ 71 ] [ 72 ] [ 73 ] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and TensorFlow's XLA (Accelerated Linear Algebra).

  9. Outline of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Outline_of_artificial...

    An intelligent machine with the versatility to perform any intellectual task. Superintelligence. A machine with a level of intelligence far beyond human intelligence. Chinese room § Strong AI. A machine that has mind, consciousness and understanding. (Also, the philosophical position that any digital computer can have a mind by running the ...