Prediction intervals provide a measure of uncertainty for predictions on regression problems. For example, a 95% prediction interval indicates that 95 out of 100 times, the true value will fall between the lower and upper values of the range. This is different from a simple point prediction that might represent the center of the uncertainty […]
It can be challenging to develop a neural network predictive model for a new dataset. One approach is to first inspect the dataset and develop ideas for what models might work, then explore the learning dynamics of simple models on the dataset, then finally develop and tune a model for the dataset with a robust […]
Weight initialization is an important design choice when developing deep learning neural network models. Historically, weight initialization involved using small random numbers, although over the last decade, more specific heuristics have been developed that use information, such as the type of activation function that is being used and the number of inputs to the node. […]
There is a lot of confusion for beginners around what algorithm is used to train deep learning neural network models. It is common to hear neural networks learn using the “back-propagation of error” algorithm or “stochastic gradient descent.” Sometimes, either of these algorithms is used as a shorthand for how a neural net is fit […]
Developing a neural network predictive model for a new dataset can be challenging. One approach is to first inspect the dataset and develop ideas for what models might work, then explore the learning dynamics of simple models on the dataset, then finally develop and tune a model for the dataset with a robust test harness. […]
Activation functions are a critical part of the design of a neural network. The choice of activation function in the hidden layer will control how well the network model learns the training dataset. The choice of activation function in the output layer will define the type of predictions the model can make. As such, a […]
Autoencoder is a type of neural network that can be used to learn a compressed representation of raw data. An autoencoder is composed of encoder and a decoder sub-models. The encoder compresses the input and the decoder attempts to recreate the input from the compressed version provided by the encoder. After training, the encoder model […]
Autoencoder is a type of neural network that can be used to learn a compressed representation of raw data. An autoencoder is composed of an encoder and a decoder sub-models. The encoder compresses the input and the decoder attempts to recreate the input from the compressed version provided by the encoder. After training, the encoder […]
Softmax is a mathematical function that converts a vector of numbers into a vector of probabilities, where the probabilities of each value are proportional to the relative scale of each value in the vector. The most common use of the softmax function in applied machine learning is in its use as an activation function in […]
AutoML refers to techniques for automatically discovering the best-performing model for a given dataset. When applied to neural networks, this involves both discovering the model architecture and the hyperparameters used to train the model, generally referred to as neural architecture search. AutoKeras is an open-source library for performing AutoML for deep learning models. The search […]
