Neural networks (also referred to a

Neural networks (also referred to as connectionist systems) are a computational approach which is based on a large collection of neural units loosely modeling the way a biological brain solves problems with large clusters of biological neurons connected by axons. Each neural unit is connected with many others, and links can be enforcing or inhibitory in their effect on the activation state of connected neural units. Each individual neural unit may have a summation function which combines the values of all its inputs together. There may be a threshold function or limiting function on each connection and on the unit itself such that it must surpass it before it can propagate to other neurons. These systems are self-learning and trained rather than explicitly programmed and excel in areas where the solution or feature detection is difficult to express in a traditional computer program.

Neural networks typically consist of multiple layers or a cube design, and the signal path traverses from front to back. Back propagation is where the forward stimulation is used to reset weights on the "front" neural units and this is sometimes done in combination with training where the correct result is known. More modern networks are a bit more free flowing in terms of stimulation and inhibition with connections interacting in a much more chaotic and complex fashion. Dynamic neural networks are the most advanced in that they dynamically can, based on rules, form new connections and even new neural units while disabling others.

The goal of the neural network is to solve problems in the same way that the human brain would, although several neural networks are much more abstract. Modern neural network projects typically work with a few thousand to a few million neural units and millions of connections, which is still several orders of magnitude less complex than the human brain and closer to the computing power of a worm.

New brain research often stimulates new patterns in neural networks. One new approach is using connections which span much further and link processing layers rather than always being localized to adjacent neurons. Other research being explored with the different types of signal over time that axons propagate which is more complex than simply on or off.

Neural networks are based on real numbers, with the value of the core and of the axon typically being a representation between 0.0 and 1.

An interesting facet of these systems is that they are unpredictable in their success with self learning. After training some become great problem solvers and others don't perform as well. In order to train them several thousand cycles of interaction typically occur.

Like other machine learning methods – systems that learn from data – neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are hard to solve using ordinary rule-based programming.

Historically, the use of neural network models marked a directional shift in the late eighties from high-level (symbolic) artificial intelligence, characterized by expert systems with knowledge embodied in if-then rules, to low-level (sub-symbolic) machine learning, characterized by knowledge embodied in the parameters of a dynamical system.

Результаты (английский) 1:

Neural networks (also referred to as connectionist systems) are a computational approach which is based on a large collection of neural units loosely modeling the way a biological brain solves problems with large clusters of biological neurons connected by axons. Each neural unit is connected with many others, and links can be enforcing or in their inhibitory effect on the activation state of the connected neural units. Each individual neural unit may have a summation function which combines the values of all its inputs together. There may be a threshold function or limiting function on each connection and on the unit itself such that it must surpass it before it can propagate to other neurons. These systems are self-learning and well-trained rather than explicitly programmed and excel in areas where the solution or feature detection is difficult to express in a traditional computer program.Neural networks typically consist of multiple layers or a cube design, and the signal path traverses from front to back. Back propagation is where the forward stimulation is used to reset weights on the "front" neural units and this is sometimes done in combination with training where the correct result is well-known. More modern networks are a bit more free flowing in terms of stimulation and inhibition with connections, interacting in a much more chaotic and complex fashion. Dynamic neural networks are the most advanced in that they can dynamically, based on rules, form new connections and even new neural units while unchecking the others.The goal of the neural network is to solve problems in the same way that the human brain would, although several neural networks are much more abstract. Modern neural network projects typically work with a few thousand to a few million units and millions of neural connections, which is still several orders of magnitude less complex than the human brain and closer to the computing power of a worm.New brain research often stimulates new patterns in neural networks. One new approach is using connections which span much further and link processing layers rather than always being localized to adjacent neurons. Other research being explored with the different types of signal over time that propagate axons which is more complex than simply on or off.Neural networks are based on real numbers, with the value of the core and of the axon typically being a representation between 0.0 and 1.An interesting facet of these systems is that they are unpredictable in their success with self learning. After training some become great problem solvers and others don't perform as well. In order to train them several thousand cycles of interaction typically occur.Like other machine learning methods-systems that learn from data-neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are hard to solve using the ordinary rule-based programming.Historically, the use of neural network models marked a directional shift in the late eighties from high-level (symbolic) characterized by artificial intelligence, expert systems with the knowledge embodied in the if-then rules, to low-level (sub-symbolic) machine learning, characterized by knowledge embodied in the parameters of a dynamical system.

Результаты (английский) 2:

Neural networks (also referred to as connectionist systems) are a computational approach which is based on a large collection of neural units loosely modeling the way a biological brain solves problems with large clusters of biological neurons connected by axons. Each neural unit is connected with many others , and links can be enforcing or inhibitory in their effect on the activation state of connected neural units. Each individual neural unit may have a summation function which combines the values of all its inputs together. There may be a threshold function or limiting function on each connection and on the unit itself such that it must surpass it before it can propagate to other neurons. These systems are self-learning and trained rather than explicitly programmed and excel in areas where the solution or feature detection is difficult to express in a traditional computer program.

Neural networks typically consist of multiple layers or a cube design, and the signal path traverses from front to back. Back propagation is where the forward stimulation is used to reset weights on the "front" neural units and this is sometimes done in combination with training where the correct result is known. More modern networks are a bit more free flowing in terms of stimulation and inhibition with connections interacting in a much more chaotic and complex fashion. Dynamic neural networks are the most advanced in that they dynamically can, based on rules, form new connections and even new neural units while disabling others.

The goal of the neural network is to solve problems in the same way that the human brain would, although several neural networks are much more abstract. Modern neural network projects typically work with a few thousand to a few million neural units and millions of connections, which is still several orders of magnitude less complex than the human brain and closer to the computing power of a worm.

New brain research often stimulates new patterns in neural networks. One new approach is using connections which span much further and link processing layers rather than always being localized to adjacent neurons. Other research being explored with the different types of signal over time that axons propagate which is more complex than simply on or off.

Based networks are the Neural on real numbers, with the of the of value and a core of the axon Typically being of a representation Between 0.0 and 1.

of An interesting facet of Systems for These That is for They are Unpredictable in Their learning by success with the self. After training some become great problem solvers and others do not perform as well. In order to train them several thousand cycles of interaction typically occur.

Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are hard to solve using ordinary rule-based programming.

Historically, the use of neural network models marked a directional shift in the late eighties from high-level (symbolic) artificial intelligence, characterized by expert systems with knowledge embodied in if-then rules, to low-level (sub-symbolic) machine learning , characterized by knowledge embodied in the parameters of a dynamical system.

Результаты (английский) 3:

Neural networks (also referred to as connectionist systems) are a computational approach which is based on a large collection of neural units loosely modeling the way a biological brain solves problems with large single of biological neurons connected by axons. each neural unit is connected with the others, and links can be enforcing or inhibitory in their effect on the activation state of connected neural units. each individual neural unit may have a summation function which combines the values of all its inputs. there may be a threshold function or limiting function on each connection and on the unit itself such that it must surpass it before it can propagate to other neurons. these systems are self - learning and trained rather than explicitly programmed and excel in areas where the solution or feature detection is difficult to express in a traditional computer program.over Neural networks consist of multiple layers or a cube design, and the signal path traverses from front to back. back propagation is where the forward stimulation is used to reset weights on the "front" neural units and this is sometimes done in combination with training where the correct result is known. more modern networks are a bit more free flowing in terms of stimulation and inhibition with connections interacting in a much more asked and complex fashion. dynamic neural networks are the most advanced in that they dynamically can, based on rules, form new connections and even new neural units while disabling others.the goal of the neural network is to solve problems in the same way that the human brain is, although several neural networks are much more abstract. modern neural network projects over work with a few thousand to a few million neural units and millions of connections, which is still several orders of magnitude less complex than the human brain, and closer to the computing power of a yard.new brain research often hair new patterns in neural networks. one new approach is using connections in europe much further and link processing layers rather than always being localized to adjacent neurons. other research being explored with the different types of signal over time that axons propagate which is more complex than simply on or off.Neural networks are based on real numbers, with the value of the core and of the axon over being a representation between 0.0 and 1.an interesting facet of these systems is that they are unpredictable in their success and self learning. after training to become great at solvers and others don't perform as well. in order to train them several thousand nature of interaction over occur.like other machine learning methods, systems that learn from data, the neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are hard to solve using ordinary rule based programming.holding, the use of neural network models with marked a shift in the late eighties from high level (symbolic) artificial intelligence, characterized by expert systems and knowledge embodied in if then rules, to low - level (sub symbolic) machine learning, characterized by knowledge embodied in the parameters of a dynamical system.