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Dilution and dropout (also called DropConnect [1]) are regularization techniques for reducing overfitting in artificial neural networks by preventing complex co-adaptations on training data. They are an efficient way of performing model averaging with neural networks. [ 2 ]
In addition to standard neural networks, Keras has support for convolutional and recurrent neural networks. It supports other common utility layers like dropout, batch normalization, and pooling. [12] Keras allows users to produce deep models on smartphones (iOS and Android), on the web, or on the Java Virtual Machine. [8]
A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
Chollet is the creator of the Keras deep-learning library, released in 2015. His research focuses on computer vision , the application of machine learning to formal reasoning , abstraction , [ 2 ] and how to achieve greater generality in artificial intelligence .
Inception v2 was released in 2015, in a paper that is more famous for proposing batch normalization. [7] [8] It had 13.6 million parameters.It improves on Inception v1 by adding batch normalization, and removing dropout and local response normalization which they found became unnecessary when batch normalization is used.
Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some ...
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Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. [1] Other frameworks in the spectrum of supervisions include weak- or semi-supervision, where a small portion of the data is tagged, and self-supervision.