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Regulatory Feedback
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. Most artificial neural networks bear only some resemblance to their more complex biological counterparts, but are very effective at their intended tasks (e.g. classification or segmentation). Some artificial neural networks are adaptive systems and are used for example to model populations and environments, which constantly change. Neural networks can be hardware- (neurons are represented by physical components) or software-based (computer models), a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Neural Network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called ''edges''. Neurons and edges typically have a ''weight'' that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Unsupervised Learning
Unsupervised learning is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and then generate imaginative content from it. In contrast to supervised learning where data is tagged by an expert, e.g. tagged as a "ball" or "fish", unsupervised methods exhibit self-organization that captures patterns as probability densities or a combination of neural feature preferences encoded in the machine's weights and activations. The other levels in the supervision spectrum are reinforcement learning where the machine is given only a numerical performance score as guidance, and semi-supervised learning where a small portion of the data is tagged. Neural networks Tasks vs. methods Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
DeepDream
DeepDream is a computer vision program created by Google engineer Alexander Mordvintsev that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dream-like appearance reminiscent of a psychedelic experience in the deliberately overprocessed images. Google's program popularized the term (deep) "dreaming" to refer to the generation of images that produce desired activations in a trained deep network, and the term now refers to a collection of related approaches. History The DeepDream software, originated in a deep convolutional network codenamed "Inception" after the film of the same name, was developed for the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in 2014 and released in July 2015. The dreaming idea and name became popular on the internet in 2015 thanks to Google's DeepDream program. The idea dates from early in the history of neural networks, and similar methods have been used to synthe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Capsule Neural Network
A capsule neural network (CapsNet) is a machine learning system that is a type of artificial neural network (ANN) that can be used to better model hierarchical relationships. The approach is an attempt to more closely mimic biological neural organization. The idea is to add structures called “capsules” to a convolutional neural network (CNN), and to reuse output from several of those capsules to form more stable (with respect to various perturbations) representations for higher capsules. The output is a vector consisting of the probability of an observation, and a pose for that observation. This vector is similar to what is done for example when doing ''classification with localization'' in CNNs. Among other benefits, capsnets address the "Picasso problem" in image recognition: images that have all the right parts but that are not in the correct spatial relationship (e.g., in a "face", the positions of the mouth and one eye are switched). For image recognition, capsnets exploi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Yann LeCun
Yann André LeCun ( , ; originally spelled Le Cun; born 8 July 1960) is a French computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics and computational neuroscience. He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University and Vice-President, Chief AI Scientist at Meta. He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. He is also one of the main creators of the DjVu image compression technology (together with Léon Bottou and Patrick Haffner). He co-developed the Lush programming language with Léon Bottou. LeCun received the 2018 Turing Award (often referred to as " Nobel Prize of Computing"), together with Yoshua Bengio and Geoffrey Hinton, for their work on deep learning. The three are sometimes referred to as the "Godfathers of AI" and "Godfathers of Deep Lea ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Visual Field
The visual field is the "spatial array of visual sensations available to observation in introspectionist psychological experiments". Or simply, visual field can be defined as the entire area that can be seen when an eye is fixed straight at a point. The equivalent concept for optical instruments and image sensors is the field of view (FOV). In optometry, ophthalmology, and neurology, a visual field test is used to determine whether the visual field is affected by diseases that cause local scotoma or a more extensive loss of vision or a reduction in sensitivity (increase in threshold). Normal limits The normal (monocular) human visual field extends to approximately 60 degrees nasally (toward the nose, or inward) from the vertical meridian in each eye, to 107 degrees temporally (away from the nose, or outwards) from the vertical meridian, and approximately 70 degrees above and 80 below the horizontal meridian. The binocular visual field is the superimposition of the two mo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Pre-processing
Data preprocessing can refer to manipulation or dropping of data before it is used in order to ensure or enhance performance, and is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects. Data-gathering methods are often loosely controlled, resulting in out-of-range values (e.g., Income: −100), impossible data combinations (e.g., Sex: Male, Pregnant: Yes), and missing values, etc. Analyzing data that has not been carefully screened for such problems can produce misleading results. Thus, the representation and quality of data is first and foremost before running any analysis. Often, data preprocessing is the most important phase of a machine learning project, especially in computational biology. If there is much irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase is more difficult. Data preparation and ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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