Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks

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Publisher ‏ : ‎ Bradford Books (March 26, 1999)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 352 pages
ISBN-10 ‏ : ‎ 0262181908
ISBN-13 ‏ : ‎ 978-0262181907
Reading age ‏ : ‎ 18 years and up
Item Weight ‏ : ‎ 1.8 pounds
Dimensions ‏ : ‎ 9.36 x 7.25 x 1.03 inches

Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks

In the world of artificial intelligence and machine learning, neural networks have become a powerful tool for solving complex problems. One common type of neural network is the feedforward artificial neural network, which consists of layers of interconnected nodes that process input data and produce output.

Supervised learning is a popular technique used in training feedforward neural networks. In this approach, the network is provided with a set of input-output pairs, known as training data, and learns to map inputs to outputs through a process of trial and error. The network adjusts its internal parameters, known as weights, based on the error between its predictions and the ground truth labels in the training data.

Neural smithing is a term used to describe the process of fine-tuning and optimizing the architecture and parameters of a neural network to improve its performance on a specific task. This involves experimenting with different network architectures, activation functions, learning rates, and regularization techniques to find the best configuration for the given problem.

Overall, neural smithing in the context of supervised learning in feedforward artificial neural networks is a crucial step in building accurate and efficient machine learning models. By carefully crafting and refining the network through iterative training and experimentation, researchers and practitioners can achieve impressive results in a wide range of applications, from image and speech recognition to natural language processing and predictive modeling.
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