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Learn Artificial Neural Network icon

Learn Artificial Neural Network

1 for Android

The description of Learn Artificial Neural Network

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Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images. They do this without any prior knowledge about cats, for example, that they have fur, tails, whiskers and cat-like faces. Instead, they automatically generate identifying characteristics from the learning material that they process.
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 from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it.
• Basic Concepts
• Building Blocks
• Learning & Adaptation
• Supervised Learning
• Unsupervised Learning
• Learning Vector Quantization
• Adaptive Resonance Theory
• Kohonen Self-Organizing Feature Maps
• Associate Memory Network
• Hopfield Networks
• Boltzmann Machine
• Brain-State-in-a-Box Network
• Optimization Using Hopfield Network
• Other Optimization Techniques
• Genetic Algorithm
• Applications of Neural Networks
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