Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback))

This book provides a solid statistical foundation for neural networks from a pattern recognition perspective. The focus is on the types of neural nets that are most widely used in practical applications, such as the multi-layer perceptron and radial basis function networks. Rather than trying to cover many different types of neural networks, Bishop thoroughly covers topics such as density estimation, error functions, parameter optimization algorithms, data pre-processing, and Bayesian methods. All topics are organized well and all mathematical foundations are explained before being applied to neural networks. The text is suitable for a graduate or advanced undergraduate level course on neural networks or for practitioners interested in applying neural networks to real-world problems. The reader is assumed to have the level of math knowledge necessary for an undergraduate science degree.

• Pattern Recognition and Machine Learning (Information Science and Statistics)
• Deep Learning (Adaptive Computation and Machine Learning series)
• Deep Learning with Python
• Neural Networks and Deep Learning: A Textbook
• Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
• Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
• Artificial Intelligence: A Modern Approach
• Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks (A Bradford Book)
• Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data