Framework for Better Deep Learning

Last Updated on August 6, 2019

Modern deep learning libraries such as Keras allow you to define and start fitting a wide range of neural network models in minutes with just a few lines of code.

Nevertheless, it is still challenging to configure a neural network to get good performance on a new predictive modeling problem.

The challenge of getting good performance can be broken down into three main areas: problems with learning, problems with generalization, and problems with predictions.

Once you have diagnosed the specific type of problem that you are having with a network, a suite of classical and modern techniques can then be selected to address the issue and improve performance.

In this post, you will discover a framework for diagnosing performance problems with deep learning models and techniques that you can use to target and improve each specific performance problem.

After reading this post, you will know:

• Defining and fitting neural networks has never been easier, although getting good performance on new problems remains challenging.
• Neural network modeling performance problems can be decomposed into learning, generalization, and prediction type problems.
• There are decades of techniques as well as modern methods that can be used
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