Ali Masri

Machine Learning libraries are getting easier and easier to work with. Their objective is to hide complex mathematical operations and offer simple APIs. Recently, I stumbled upon MindsDb. A new platform that enables users to train models with only one line of code! I know it sounds a little bit... [Read More]

In this part we will focus on classification. Generally speaking, classification is the process of identifying to which predefined set of categories a new observation belongs. Building a classification neural network requires some tweaks to what we have done before. Let us investigate the process next.

Training neural networks is tricky. One should be careful that his model is good enough to learn from existing data, and good enough to generalize to unseen data. The lack to generalize a model is mainly because of a problem called overfitting.

After two introductory tutorials, its time to build our first neural network! The network we are building solves a simple regression problem. Regression is a process where a model learns to predict a continuous value output for a given input data, e.g. predict price, length, width, etc.

Training deep learning models requires data… A lot of data! Unfortunately, in most cases data comes messy, and our models are very sensitive towards this. Therefore, we need to be careful while preparing our data to achieve the best results.