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  2. Attention (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Attention_(machine_learning)

    As hand-crafting weights defeats the purpose of machine learning, the model must compute the attention weights on its own. Taking analogy from the language of database queries, we make the model construct a triple of vectors: key, query, and value. The rough idea is that we have a "database" in the form of a list of key-value pairs.

  3. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    The middle (hidden) layer is connected to these context units fixed with a weight of one. [51] At each time step, the input is fed forward and a learning rule is applied. The fixed back-connections save a copy of the previous values of the hidden units in the context units (since they propagate over the connections before the learning rule is ...

  4. Exponential smoothing - Wikipedia

    en.wikipedia.org/wiki/Exponential_smoothing

    Exponential smoothing puts substantial weight on past observations, so the initial value of demand will have an unreasonably large effect on early forecasts. This problem can be overcome by allowing the process to evolve for a reasonable number of periods (10 or more) and using the average of the demand during those periods as the initial forecast.

  5. 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 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]

  6. Torch (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Torch_(machine_learning)

    It also has StochasticGradient class for training a neural network using stochastic gradient descent, although the optim package provides much more options in this respect, like momentum and weight decay regularization.

  7. Principal component analysis - Wikipedia

    en.wikipedia.org/wiki/Principal_component_analysis

    Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.

  8. Feature hashing - Wikipedia

    en.wikipedia.org/wiki/Feature_hashing

    Therefore, the bags of words for a set of documents is regarded as a term-document matrix where each row is a single document, and each column is a single feature/word; the entry i, j in such a matrix captures the frequency (or weight) of the j 'th term of the vocabulary in document i. (An alternative convention swaps the rows and columns of ...

  9. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Weight Lifting Exercises monitored with Inertial Measurement Units Five variations of the biceps curl exercise monitored with IMUs. Some statistics calculated from raw data. 39,242 Text Classification 2013 [178] [179] W. Ugulino et al. sEMG for Basic Hand movements Dataset Two databases of surface electromyographic signals of 6 hand movements ...