Neural Networks: Tricks of the Trade

Many algorithms are available to learn deep hierarchies of features from unlabeled data, especially images. In many cases, these algorithms involve multi-layered networks of features (eg, neural networks) that are sometimes tricky to train and tune and are difficult. Abstract A commonly encountered problem in MLP (multi-layer perceptron) classification problems is related to the prior probabilities of the individual classes-if the number of training examples that correspond to each class varies significantly between the classes, then. Abstract Validation can be used to detect when overfitting starts during supervised training of a neural network; training is then stopped before convergence to avoid the overfitting (â œearly stoppingâ ). The exact criterion used for validation-based early stopping, however, is usually. Abstract Reservoir computing has emerged in the last decade as an alternative to gradient descent methods for training recurrent neural networks. Echo State Network (ESN) is one of the key reservoir computing â œflavorsâ . While being practical, conceptually simple, and easy. Preface In many cases, the amount of labeled data is limited and does not allow for fully identifying the function that needs to be learned. When labeled data is scarce, the learning algorithm is exposed to simultaneous underfitting and overfitting. The learning algorithm. Abstract Restricted Boltzmann machines (RBMs) have been used as generative models of many different types of data. RBMs are usually trained using the contrastive divergence learning procedure. This requires a certain amount of practical experience to decide.A commonly encountered problem in MLP (multi-layer perceptron) classification problems is related to the prior probabilities of the individual classes-if the number of training examples that correspond to each class varies significantly between the classes, then. Abstract Validation can be used to detect when overfitting starts during supervised training of a neural network; training is then stopped before convergence to avoid the overfitting (â œearly stoppingâ ). The exact criterion used for validation-based early stopping, however, is usually. Abstract Reservoir computing has emerged in the last decade as an alternative to gradient descent methods for training recurrent neural networks. Echo State Network (ESN) is one of the key reservoir computing â œflavorsâ . While being practical, conceptually simple, and easy. Preface In many cases, the amount of labeled data is limited and does not allow for fully identifying the function that needs to be learned. When labeled data is scarce, the learning algorithm is exposed to simultaneous underfitting and overfitting. The learning algorithm. Abstract Restricted Boltzmann machines (RBMs) have been used as generative models of many different types of data. RBMs are usually trained using the contrastive divergence learning procedure. This requires a certain amount of practical experience to decide. It is our belief that researchers and practitioners acquire, through experience and word-ofmouth, techniques and heuristics that help them successfully apply neural networks to di cult real world problems. Often these\ tricks" are theo-tically well motivated. Sometimes they. Abstract The convergence of back-propagation learning is analyzed so as to explain common phenomenon observed by practitioners. Many undesirable behaviors of backprop can be avoided with tricks that are rarely exposed in serious technical publications. This. Abstract Chapter 1 strongly advocates the stochastic back-propagation method to train neural networks. This is in fact an instance of a more general technique called stochastic gradient descent (SGD). This chapter provides background material, explains why SGD is a. Abstract WeChapter 1 strongly advocates the stochastic back-propagation method to train neural networks. This is in fact an instance of a more general technique called stochastic gradient descent (SGD). This chapter provides background material, explains why SGD is a. Abstract We show how nonlinear semi-supervised embedding algorithms popular for use with â œshallowâ learning techniques such as kernel methods can be easily applied to deep multi-layer architectures, either as a regularizer at the output layer, or on each layer.