Archive | Deep Learning Performance

Example of Train and Validation Learning Curves Showing a Training Dataset That May Be too Small Relative to the Validation Dataset

How to use Learning Curves to Diagnose Machine Learning Model Performance

A learning curve is a plot of model learning performance over experience or time. Learning curves are a widely used diagnostic tool in machine learning for algorithms that learn from a training dataset incrementally. The model can be evaluated on the training dataset and on a hold out validation dataset after each update during training […]

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Practical Recommendations for Deep Learning Neural Network Practitioners

Recommendations for Deep Learning Neural Network Practitioners

Deep learning neural networks are relatively straightforward to define and train given the wide adoption of open source libraries. Nevertheless, neural networks remain challenging to configure and train. In his 2012 paper titled “Practical Recommendations for Gradient-Based Training of Deep Architectures” published as a preprint and a chapter of the popular 2012 book “Neural Networks: […]

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8 Tricks for Configuring Backpropagation to Train Better Neural Networks, Faster

8 Tricks for Configuring Backpropagation to Train Better Neural Networks

Neural network models are trained using stochastic gradient descent and model weights are updated using the backpropagation algorithm. The optimization solved by training a neural network model is very challenging and although these algorithms are widely used because they perform so well in practice, there are no guarantees that they will converge to a good […]

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A Gentle Introduction to the Challenge of Training Deep Learning Neural Network Models

A Gentle Introduction to the Challenge of Training Deep Learning Neural Network Models

Deep learning neural networks learn a mapping function from inputs to outputs. This is achieved by updating the weights of the network in response to the errors the model makes on the training dataset. Updates are made to continually reduce this error until either a good enough model is found or the learning process gets […]

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