After building a successful machine learning model and deploying to production. Maybe after few months you gathered a new set of training data and you want to improve the metrics of your model. …

Most beginner machine learning practictioners get confused with these two metrics that are the heart building any successful ML model. Task: Classify cat/not-cat After classifying bunch of images: Precision: Just take the ones that are predicted as cat. How many of those images were actually cat? Recall: How many % of cats were correctly recognised by the model? Which one is better? It seems both of them needs to be better metrics for our model. But should precision be high or should recall be high? Or should both of the them be high?

Flow zone is that state of mind in which a programmer is highly focused on solving a particular problem. His brain is fully concentrated on the problem and he is disconnected from the rest of the world. When he gets out of the zone, he gets the feeling of getting…

According to Uncle bob, there are three rules that should be followed to get the best results from TDD. They are: You should write only enough tests that are sufficient to fail You should only write enough code that is sufficient to pass the written failing code Write production code…

A lot of things have changed in recent years in the tech industry. It is getting harder to cope with the relevant skills and expertise to sustain the fast-growing tech industry. With the burst of numerous frameworks and programming languages, now we have tools to solve every different kind of…

Working on a large codebase is tough. Even a small change can cause many side-effects. The fear of not changing the codebase due to the fear of these consequences is even more harmful to the codebase in long run. The fear is because of lacking test cases. …

It is practically impossible to write software without bugs but it is even harder to foresee the side effects to make sure everything is in order. …

Overfitting can occur due to different reasons. There are some ways to prevent this and improve your model. Some of them are: Perform early stopping Early stopping is one of the effective methods to try when you notice that the validation loss increases while training loss decreases. …

Building a successful ML model can involve twisting and tweaking hyperparameters that give the best model for the dataset you are working with. But among all other hyperparameters, there are some of them that are present in almost any machine learning or deep learning algorithms and play an important role…