Artificial neural networks refer to the computing systems inspired by biological neural networks. They are based on nodes or artificial neurons, which are a replica of biological neurons found in the brain of animals. This enables them to learn and thereby perform tasks by considering examples. The use of artificial neural networks is vast as they are applied in varied fields like medical diagnosis, speech recognition, computer vision, machine translation, etc. Some common variants include convolutional neural networks, ...
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Artificial neural networks refer to the computing systems inspired by biological neural networks. They are based on nodes or artificial neurons, which are a replica of biological neurons found in the brain of animals. This enables them to learn and thereby perform tasks by considering examples. The use of artificial neural networks is vast as they are applied in varied fields like medical diagnosis, speech recognition, computer vision, machine translation, etc. Some common variants include convolutional neural networks, deep stacking networks, deep belief networks, deep predictive coding networks, etc. The theoretical properties of artificial neural networks are capacity, generalization and statistics, computational power, convergence, etc. This book is a valuable compilation of topics, ranging from the basic to the most complex advancements in the field of artificial neural networks. The book attempts to assist those with a goal of delving into this field. The various studies that are constantly contributing towards advancing technologies and evolution of this field are examined in detail.
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