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Deep Neural Network
Deep learning is a subset of machine learning that focuses on utilizing multilayered neural network (machine learning), neural networks to perform tasks such as Statistical classification, classification, Regression analysis, regression, and representation learning. The field takes inspiration from Neuroscience, biological neuroscience and is centered around stacking Artificial neuron, artificial neurons into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers (ranging from three to several hundred or thousands) in the network. Methods used can be either Supervised learning, supervised, Semi-supervised learning, semi-supervised or Unsupervised learning, unsupervised. Some common deep learning network architectures include Fully connected network, fully connected networks, deep belief networks, recurrent neural networks, convolutional neural networks, Generative adversarial network, generative adversarial networks, Transformer (ma ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Deep Learning
Deep learning is a subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers (ranging from three to several hundred or thousands) in the network. Methods used can be either supervised, semi-supervised or unsupervised. Some common deep learning network architectures include fully connected networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance fields. These architectures have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neural Radiance Field
A neural radiance field (NeRF) is a method based on deep learning for reconstructing a three-dimensional representation of a scene from two-dimensional images. The NeRF model enables downstream applications of novel view synthesis, scene geometry reconstruction, and obtaining the reflectance properties of the scene. Additional scene properties such as camera poses may also be jointly learned. First introduced in 2020, it has since gained significant attention for its potential applications in computer graphics and content creation. Algorithm The NeRF algorithm represents a scene as a radiance field parametrized by a deep neural network (DNN). The network predicts a volume density and view-dependent emitted radiance given the spatial location (x,y,z) and viewing direction in Euler angles (\theta, \Phi) of the camera. By sampling many points along camera rays, traditional volume rendering techniques can produce an image. Data collection A NeRF needs to be retrained for each ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Transformer (neural Network)
The transformer is a deep learning architecture based on the multi-head Attention (machine learning), attention mechanism, in which text is converted to numerical representations called Large language model#Tokenization, tokens, and each token is converted into a vector via lookup from a word embedding table. At each layer, each Tokenization (lexical analysis), token is then Contextualization (computer science), contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism, allowing the signal for key tokens to be amplified and less important tokens to be diminished. Transformers have the advantage of having no recurrent units, therefore requiring less training time than earlier Recurrent neural network, recurrent neural architectures (RNNs) such as long short-term memory (LSTM). Later variations have been widely adopted for training large language models (LLM) on large (language) Training, validation, and test da ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Convolutional Neural Network
A convolutional neural network (CNN) is a type of feedforward neural network that learns features 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. Convolution-based networks are the de-facto standard in deep learning-based approaches to computer vision and image processing, and have only recently been replaced—in some cases—by newer deep learning architectures such as the transformer. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by the regularization that comes from using shared 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. However, applying cascaded ''convolution'' (or cross-correlation) kernels, only 25 weights for each convolutio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Human Brain
The human brain is the central organ (anatomy), organ of the nervous system, and with the spinal cord, comprises the central nervous system. It consists of the cerebrum, the brainstem and the cerebellum. The brain controls most of the activities of the human body, body, processing, integrating, and coordinating the information it receives from the sensory nervous system. The brain integrates sensory information and coordinates instructions sent to the rest of the body. The cerebrum, the largest part of the human brain, consists of two cerebral hemispheres. Each hemisphere has an inner core composed of white matter, and an outer surface – the cerebral cortex – composed of grey matter. The cortex has an outer layer, the neocortex, and an inner allocortex. The neocortex is made up of six Cerebral cortex#Layers of neocortex, neuronal layers, while the allocortex has three or four. Each hemisphere is divided into four lobes of the brain, lobes – the frontal lobe, frontal, pa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Biological System
A biological system is a complex Biological network inference, network which connects several biologically relevant entities. Biological organization spans several scales and are determined based different structures depending on what the system is. Examples of biological systems at the macro scale are populations of organisms. On the organ (anatomy), organ and Tissue (biology), tissue scale in mammals and other animals, examples include the circulatory system, the respiratory system, and the nervous system. On the Micrometre, micro to the Nanometre, nanoscopic scale, examples of biological systems are cell (biology), cells, organelles, macromolecular complexes and Regulatory T cell, regulatory pathways. A biological system is not to be confused with a Living systems, living system, such as a living organism. Organ and tissue systems These specific systems are widely studied in human anatomy and are also present in many other animals. * Respiratory system: the organs used for b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Board Game
A board game is a type of tabletop game that involves small objects () that are placed and moved in particular ways on a specially designed patterned game board, potentially including other components, e.g. dice. The earliest known uses of the term "board game" are between the 1840s and 1850s. While game boards are a necessary and sufficient condition of this genre, card games that do not use a standard deck of cards, as well as games that use neither cards nor a game board, are often colloquially included, with some referring to this genre generally as "table and board games" or simply "tabletop games". Eras Ancient era Board games have been played, traveled, and evolved in most cultures and societies throughout history Board games have been discovered in a number of archaeological sites. The oldest discovered gaming pieces were discovered in southwest Turkey, a set of elaborate sculptured stones in sets of four designed for a chess-like game, which were created during the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Climatology
Climatology (from Greek , ''klima'', "slope"; and , '' -logia'') or climate science is the scientific study of Earth's climate, typically defined as weather conditions averaged over a period of at least 30 years. Climate concerns the atmospheric condition during an extended to indefinite period of time; weather is the condition of the atmosphere during a relative brief period of time. The main topics of research are the study of climate variability, mechanisms of climate changes and modern climate change. This topic of study is regarded as part of the atmospheric sciences and a subdivision of physical geography, which is one of the Earth sciences. Climatology includes some aspects of oceanography and biogeochemistry. The main methods employed by climatologists are the analysis of observations and modelling of the physical processes that determine climate. Short term weather forecasting can be interpreted in terms of knowledge of longer-term phenomena of climate, for insta ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Medical Image Analysis
Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care. The main goal of MIC is to extract clinically relevant information or knowledge from medical images. While closely related to the field of medical imaging, MIC focuses on the computational analysis of the images, not their acquisition. The methods can be grouped into several broad categories: image segmentation, image registration, image-based physiological modeling, and others. Data forms Medical image computing typically operates on uniformly sampled data with regular x-y-z spatial spacing (images in 2D and volumes in 3D, generically referred to as images). At each sample point, data is commonly represented in Integer (computer sci ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Drug Design
Drug design, often referred to as rational drug design or simply rational design, is the invention, inventive process of finding new medications based on the knowledge of a biological target. The drug is most commonly an organic compound, organic small molecule that activates or inhibits the function of a biomolecule such as a protein, which in turn results in a therapeutic effect, therapeutic benefit to the patient. In the most basic sense, drug design involves the design of molecules that are complementary in shape and electric charge, charge to the biomolecular target with which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on molecular modelling, computer modeling techniques. This type of modeling is sometimes referred to as computer-aided drug design. Finally, drug design that relies on the knowledge of the three-dimensional structure of the biomolecular target is known as structure-based drug design. In addition to small molec ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bioinformatics
Bioinformatics () is an interdisciplinary field of science that develops methods and Bioinformatics software, software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, data science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The process of analyzing and interpreting data can sometimes be referred to as computational biology, however this distinction between the two terms is often disputed. To some, the term ''computational biology'' refers to building and using models of biological systems. Computational, statistical, and computer programming techniques have been used for In silico, computer simulation analyses of biological queries. They include reused specific analysis "pipelines", particularly in the field of genomics, such as by the identification of genes and single nucleotide polymorphis ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Translation
Machine translation is use of computational techniques to translate text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. Early approaches were mostly rule-based or statistical. These methods have since been superseded by neural machine translation and large language models. History Origins The origins of machine translation can be traced back to the work of Al-Kindi, a ninth-century Arabic cryptographer who developed techniques for systemic language translation, including cryptanalysis, frequency analysis, and probability and statistics, which are used in modern machine translation. The idea of machine translation later appeared in the 17th century. In 1629, René Descartes proposed a universal language, with equivalent ideas in different tongues sharing one symbol. The idea of using digital computers for translation of natural languages was proposed as early as 1947 by England's A. D. Booth and Warr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
