Skills in deep learning are in great demand, although these skills can be challenging to identify and to demonstrate. Explaining […]
Archive | Deep Learning Performance
Why Training a Neural Network Is Hard
Or, Why Stochastic Gradient Descent Is Used to Train Neural Networks. Fitting a neural network involves using a training 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 […]
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, […]
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 […]
Neural Networks: Tricks of the Trade Review
Deep learning neural networks are challenging to configure and train. There are decades of tips and tricks spread across hundreds […]
How to Get Better Deep Learning Results (7-Day Mini-Course)
Better Deep Learning Neural Networks Crash Course. Get Better Performance From Your Deep Learning Models in 7 Days. Configuring neural […]
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 […]
How to Control Neural Network Model Capacity With Nodes and Layers
The capacity of a deep learning neural network model controls the scope of the types of mapping functions that it […]
Framework for Better Deep Learning
Modern deep learning libraries such as Keras allow you to define and start fitting a wide range of neural network […]