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Visual Analysis of Maya Glyphs via Crowdsourcing and Deep Learning
Related concepts (67)
Maya script
Maya script, also known as Maya glyphs, is historically the native writing system of the Maya civilization of Mesoamerica and is the only Mesoamerican writing system that has been substantially deciphered. The earliest inscriptions found which are identifiably Maya date to the 3rd century BCE in San Bartolo, Guatemala. Maya writing was in continuous use throughout Mesoamerica until the Spanish conquest of the Maya in the 16th and 17th centuries.
Deep learning
Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Maya civilization
The Maya civilization (ˈmaɪə) was a Mesoamerican civilization that existed from antiquity to the early modern period. It is known by its ancient temples and glyphs (script). The Maya script is the most sophisticated and highly developed writing system in the pre-Columbian Americas. The civilization is also noted for its art, architecture, mathematics, calendar, and astronomical system. The Maya civilization developed in the Maya Region, an area that today comprises southeastern Mexico, all of Guatemala and Belize, and the western portions of Honduras and El Salvador.
Convolutional neural network
Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.
Types of artificial neural networks
There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.
Feedforward neural network
A feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. Its flow is uni-directional, meaning that the information in the model flows in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes, without any cycles or loops, in contrast to recurrent neural networks, which have a bi-directional flow.
Maya codices
Maya codices (singular codex) are folding books written by the pre-Columbian Maya civilization in Maya hieroglyphic script on Mesoamerican bark paper. The folding books are the products of professional scribes working under the patronage of deities such as the Tonsured Maize God and the Howler Monkey Gods. Most of the codices were destroyed by conquistadors and Catholic priests in the 16th century. The codices have been named for the cities where they eventually settled.
Transformer (machine learning model)
A transformer is a deep learning architecture that relies on the parallel multi-head attention mechanism. The modern transformer was proposed in the 2017 paper titled 'Attention Is All You Need' by Ashish Vaswani et al., Google Brain team. It is notable for requiring less training time than previous recurrent neural architectures, such as long short-term memory (LSTM), and its later variation has been prevalently adopted for training large language models on large (language) datasets, such as the Wikipedia corpus and Common Crawl, by virtue of the parallelized processing of input sequence.
Data and information visualization
Data and information visualization (data viz or info viz) is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items.
Deep belief network
In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. When trained on a set of examples without supervision, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as feature detectors. After this learning step, a DBN can be further trained with supervision to perform classification.