{'fraction_non_alphanumeric': 0.020781637717121586, 'fraction_numerical': 0.00620347394540943, 'mean_word_length': 5.54158215010142, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Fundamentals of Artificial Neural Networks [Book Reviews]

Artificial neural networks are systems motivated by the distributed, massively parallel computation in the brain that enables it to be so successful at complex control and recognitiodclassification tasks. The biological neural network that accomplishes this can be mathematically modeledcaricatured by a weighted, directed graph of highly interconnected nodes (neurons). The artificial nodes are almost always simple transcendental functions whose arguments are the weighted summation of the inputs to the node; early work on neural networks and some current work uses node functions taking on only binary values. After a period of active development in the 1950’s and 1960’s, that slowed in the face of the limitations of the networks then being explored, neural networks experienced a renaissance in the 1980’s with the work of Hopfield [7] on the use of networks with feedback (graphs with cycles) as associative memories, and that of Rumelhart et al. [13] on backpropagation training and feedforward (acyclic graphs) networks that could “leam” from input-output examples provided in a training set. Learning in this context is carried out by a descent-based algorithm that adjusts the network weights so that the network response closely approximates the desired responses specified by the training set. This ability to learn from training data, rather than needing to be explicitly (heuristically) programmed, was important both for an understanding of the functioning of brains and for progress in a great variety of applications in which practitioners had been unable to embed their qualitative understanding in successful programs. The capabilities of neural networks were quickly exploited in a great number of applications to pattern classification, control, and time-series forecasting. Hopfield’s work on associative memories excited the interest of his fellow statistical physicists who felt that their methods of analysis would be applicable and productive in studying the asymptotic behavior of neural networks. Unfortunately, many of the applications and studies were either trivial or misguided and earned the field the sobriquet of “hype” for the common appearance of exaggerated claims. Nonetheless, information theory distinguished itself with such solid papers as that of McEliece et al. [8] in providing mathematically sophisticated analyses of network capabilities. The 1990’s saw a significant maturation both in applications and in theoretical understanding of performance and limitations. In particular, neural networks provided a wide spectrum of applied statisticians with a new and powerful class of regression and classification functions that, for the first time, allowed them to make successful truly nonlinear models involving hundreds of variables. The problem of “feature” or “regressor” selection becomes less critical when you do not need to narrow your choices among input variables. A new regime in statistics became accessible, and applied statisticians were no longer restricted in practice either to very simple nonlinear models in a few variables or to larger, but linear, models based solely upon second-order properties.