{ "item_title" : "Physical Models of Neural Networks", "item_author" : [" Tamás Geszti "], "item_description" : "This lecture note volume is mainly about the recent development that connected neural network modeling to the theoretical physics of disordered systems. It gives a detailed account of the (Little-) Hopfield model and its ramifications concerning non-orthogonal and hierarchical patterns, short-term memory, time sequences, and dynamical learning algorithms. It also offers a brief introduction to computation in layered feed-forward networks, trained by back-propagation and other methods. Kohonen's self-organizing feature map algorithm is discussed in detail as a physical ordering process. The book offers a minimum complexity guide through the often cumbersome theories developed around the Hopfield model. The physical model for the Kohonen self-organizing feature map algorithm is new, enabling the reader to better understand how and why this fascinating and somewhat mysterious tool works.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/81/020/012/9810200129_b.jpg", "price_data" : { "retail_price" : "76.00", "online_price" : "76.00", "our_price" : "76.00", "club_price" : "76.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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