Deep Learning

Loss vs Accuracy

A loss function is used to optimize the model (e.g. a neural network) you’ve built to solve a problem.

Data Science

Loss functions

In machine learning, the difference between the predicted output and the actual output is used to tune the parameters of the algorithm. This error in prediction, so called...

Data Science

Methods of Hyperparameter optimization

The parameters, called hyperparameters, that define the performance of the machine learning algorithm (model), depends on the problem we are trying to solve. Thus, they need to be...

Mathematics

A visual introduction to eigenvectors and eigenvalues

The word eigen, having a German origin, means characteristics. The eigenvalues and eigenvectors give the characteristic, but of what? Let’s understand it through a geometric example.

Data Science

Scaling vs Normalization

Feature scaling (also known as data normalization) is the method used to standardize the range of features of data. Since, the range of values of data may vary...

Data Science

Ensembling is the key

Most of us have our favourite machine learning algorithms. For some, it may be state-of-the-art algos like Support Vector Machines while for others it may be something simple...

Data Science

Gradient boosted trees: Better than random forest?

Does gradient boosted trees generally perform better than random forest? Let’s see that. But, first what are these methods? Random forest and boosting are ensemble methods, proved to...

Data Science

Data Mining: Knowledge discovery in databases

Knowledge discovery in databases (KDD) is a 7 step process to search for hidden knowledge in data. Data Mining refers to the analysis step in the KDD process....

Data Science

The Curse of Dimensionality

While applying k nearest neighbors approach in solving a problem, we can sometimes notice that there is a deterioration in the kNN performance when the number of predictors,...

Data Science

Regularization

Our machine learning model often encouters the problem of overfitting. Regularization is one of the techniques to solve this problem.

Data Science

Simplicity doesn't imply accuracy

Often, people say things like beauty lies in simplicity, simplicity is the glory of expression, complexity is the enemy of execution. But to what extent, these statements are...

Data Science

Overfitting and Underfitting

In machine learning, sometimes the prediction of our model may not be satisfactory. Although there may be many reasons for that, often it is due to either overfitting...

Data Science

Email spam filtering: Text analysis in R

Email spam1, also known as junk email, is a type of electronic spam where unsolicited messages are sent by email.

Data Science

Moneyball: Why no prediction can't be made for baseball champion

Data Science

Moneyball: How linear regression changed baseball

It’s unbelievable how much you don’t know about the game you’ve been playing all your life. — Mickey Mantle