Deep learning algorithms require a huge amount of training data. This makes us put more and more labeled data into our training set even if it does not belong to the same distribution of data we are actually interested in. For example, let's say we are building a cat classifier for door camera devices. We … Continue reading What to do when we have mismatched training and validation set?
As promised, this is the second post on my two part blog series on time series modelling and forecasting. In my first blog post I discussed the basics of time series analysis and gave a theoretical overview. In case you missed it you can find it here - Understanding Time Series Modelling and Forecasting, Part 1 … Continue reading Understanding Time Series Modelling and Forecasting, Part 2
Time series forecasting is extensively used in numerous practical fields such as business, economics, finance, science and engineering. The main aim of a time series analysis is to forecast future values of a variable using its past values. In this post, I will give you a detailed introduction to time series modelling. This would be the … Continue reading Understanding Time Series Modelling and Forecasting – Part 1
In this post, I will discuss a very common problem that we face when dealing with a machine learning task - How to handle categorical data especially when the entire dataset is too large to fit in memory? I will talk about how to represent categorical variables, the common problems we face while one hot … Continue reading How to One Hot Encode Categorical Variables of a Large Dataset in Python?