https://medium.com/@sahildhankhad/five-methods-to-debug-your-neural-network-6fc7dc686929

A lot of us trying to understand the Machine Learning Algorithms, but sometimes we have some time we faced bugging problem in our algorithm and that what we are going to find out how to debug your Neural Network. This article is short but well documented about the debugging process.So, I expect all of you know what Neural Network is and how it is work. But I’m still going to talk a little bit about the Neural Network, and you can skip this part if you are familiar with Neural Network Concepts.

Neural networks are a collection of algorithms, modelled loosely after the human brain, that is designed to recognize patterns. They interpret sensory data through a variety of machine perception, labelling or clustering raw input. The models they understand are numerical, included in vectors.Neural networks benefit us to cluster and classify. You can consider them as a clustering and classification layer on top of the data you store and manage. They support group unlabeled data according to relationships among the example inputs, and they classify data when they have a labelled dataset to train in. In many events, researchers will face a problem: the neural network implemented using the machine learning framework may be far removed from the theoretical model. To examine whether the model is reliable, the direct way is to continually correct and adjust. For example, in August 2018, Ian Goodfellow and others at Google Brain introduced TensorFuzz, they added an open source library that helps to automatically debug neural networks by submitting coverage-guided fuzzing (CGF).It’s not simple to debug a machine learning model, because the cost of discovering a bug is too high. Even for almost simple feedforward neural networks, researchers need to discuss issues such as network architecture, weight initialization, and network optimization.