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Some focus on the human brain, others on non-human. As the number of databases that seek to disseminate information about the structure, development and function of the brain has grown, so has the need to collate these resources themselves. As a result, there now exist databases of neuroscience databases, some of which reach over 3000 entries. [1]
Lung Cancer Dataset Lung cancer dataset without attribute definitions 56 features are given for each case 32 Text Classification 1992 [270] [271] Z. Hong et al. Arrhythmia Dataset Data for a group of patients, of which some have cardiac arrhythmia. 276 features for each instance. 452 Text Classification 1998 [272] [273] H. Altay et al.
The Brain Imaging Data Structure (BIDS) is a standard for organizing, annotating, and describing data collected during neuroimaging experiments. It is based on a formalized file and directory structure and metadata files (based on JSON and TSV ) with controlled vocabulary . [ 1 ]
Magenta is a project that uses Google Brain to create new information in the form of art and music rather than classify and sort existing data. [2] TensorFlow was updated with a suite of tools for users to guide the neural network to create images and music. [ 2 ]
Metastatic brain cancer is over six times more common than primary brain cancer, as it occurs in about 10–30% of all people with cancer. [1] This is a list of notable people who have had a primary or metastatic brain tumor (either benign or malignant) at some time in their lives, as confirmed by public information. Tumor type and survival ...
Moreover, if whole brain emulation is possible via both scanning and replicating the, at least, bio-chemical brain – as premised in the form of digital replication in The Age of Em, possibly using physical neural networks – that may have applications as or more extensive than e.g. valued human activities and may imply that society would ...
[5] [42] Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a ...
Long short-term memory (LSTM) [1] is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem [2] commonly encountered by traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models , and other sequence learning methods.