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

Last Updated on August 6, 2019

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 stuck and stops.

The process of training neural networks is the most challenging part of using the technique in general and is by far the most time consuming, both in terms of effort required to configure the process and computational complexity required to execute the process.

In this post, you will discover the challenge of finding model parameters for deep learning neural networks.

After reading this post, you will know:

• Neural networks learn a mapping function from inputs to outputs that can be summarized as solving the problem of function approximation.
• Unlike other machine learning algorithms, the parameters of a neural network must be found by solving a non-convex optimization problem with many good solutions and many misleadingly good solutions.
• The stochastic gradient descent algorithm is used to solve the optimization problem where model parameters are updated
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