Deep learning neural networks are trained using the stochastic gradient descent optimization algorithm. As part of the optimization algorithm, the error for the current state of the model must be estimated repeatedly. This requires the choice of an error function, conventionally called a loss function, that can be used to estimate the loss of the […]
Neural networks are trained using stochastic gradient descent and require that you choose a loss function when designing and configuring your model. There are many loss functions to choose from and it can be challenging to know what to choose, or even what a loss function is and the role it plays when training a […]
Deep learning neural networks are trained using the stochastic gradient descent optimization algorithm. The learning rate is a hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated. Choosing the learning rate is challenging as a value too small may result in a […]
The weights of a neural network cannot be calculated using an analytical method. Instead, the weights must be discovered via an empirical optimization procedure called stochastic gradient descent. The optimization problem addressed by stochastic gradient descent for neural networks is challenging and the space of solutions (sets of weights) may be comprised of many good […]
Neural networks are trained using gradient descent where the estimate of the error used to update the weights is calculated based on a subset of the training dataset. The number of examples from the training dataset used in the estimate of the error gradient is called the batch size and is an important hyperparameter that […]
Batch normalization is a technique designed to automatically standardize the inputs to a layer in a deep learning neural network. Once implemented, batch normalization has the effect of dramatically accelerating the training process of a neural network, and in some cases improves the performance of the model via a modest regularization effect. In this tutorial, […]
Practical deep learning is a challenging subject in which to get started. It is often taught in a bottom-up manner, requiring that you first get familiar with linear algebra, calculus, and mathematical optimization before eventually learning the neural network techniques. This can take years, and most of the background theory will not help you to […]
Training deep neural networks with tens of layers is challenging as they can be sensitive to the initial random weights and configuration of the learning algorithm. One possible reason for this difficulty is the distribution of the inputs to layers deep in the network may change after each mini-batch when the weights are updated. This […]
Developing neural networks is often referred to as a dark art. The reason for this is that being skilled at developing neural network models comes from experience. There are no reliable methods to analytically calculate how to design a “good” or “best” model for your specific dataset. You must draw on experience and experiment in […]
The vanishing gradients problem is one example of unstable behavior that you may encounter when training a deep neural network. It describes the situation where a deep multilayer feed-forward network or a recurrent neural network is unable to propagate useful gradient information from the output end of the model back to the layers near the […]
