Predicting Stock Market

In this project, we’ll be working with data from the S&P500 Index. We will be using historical data on the price of the S&P500 Index to make predictions about future prices. Predicting whether an index will go up or down will help us forecast how the stock market as a…

Ever wonder where Gaussian distribution is used in a real world?

Consider or imagine as you are working in a company named XYZ. You are working as a data analyst or as a data scientist or even as a HR manager and your…

Why learn Linear Algebra ?

As you have already studied about linear algebra in your high school and college. Linear algebra is fundamental to almost every field of mathematics. And for the reminder, Linear algebra is the mathematical branch of linear equations such as Linear maps. And their representations by matrices and in vector spaces…

How to choose your Satisficing and optimizing metric ?

There are different metrics for assessing a classifier ‘s performance, they are called evaluation matrices. They can be categorized as matrices that satisfy and optimize. It is necessary to remember that these evaluation matrices must be evaluated on a training set, a production set or on the test set.

Example…

Why use Single Number Evaluation Metric in Machine Learning?

If you are tuning hyper-parameters, or attempting various ideas for Learning algorithms, or trying out various options for constructing your learning machine. You will notice your development would be much quicker if you have Single Number Evaluation Metric that quickly lets you tell whether the unknown thing you are doing…

Orthogonalization in Machine Learning

Orthogonalization is a system design property that ensures that modification of an instruction or an algorithm component does not create or propagate side effects to other system components. Orthogonalization makes it easier to independently verify the algorithms, thus reducing the time required for testing and development.

One of the problems…

Hyperparameters tuning in practice: Pandas vs. Caviar

Today deep learning is applied to several different areas of application, and intuitions about hyperparameter settings from one area of application can or may not move to another. There’s a lot of mixing between the domains of different applications. …

Using an appropriate scale to pick hyperparameters

In the last aticle you saw how random sampling, over the range of hyperparameters, can enable you to more efficiently search the space of hyperparameters. Yet it turns out that random sampling doesn’t imply uniform sampling at random, over the spectrum of valid values.Instead, … 