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…
This article is about the simple example where you can use Gaussian distribution in the real world scenario.
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…
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…
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…
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…
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. …
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, …
Optimization or tuning of hyper-parameters is the question of choosing an appropriate range of hyper-parameters for a learning algorithm. A hyper-parameter is a parameter of which its value controls the learning process. The values of other parameters (usually node weights) are learned, by contrast.
One of the frustrating things about…
Adam optimization algorithm is one of the unique algorithms that has really stood up and proven to be effective well across a wide variety of models in deep learning. Adam optimization algorithm essentially takes and ties together Momentum and RMSprop. Adams stands for Adaptive Moment Estimation.
How it works ?
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Bibek Shah Shankhar
I post articles on Data Science | Machine Learning | Deep Learning . Connect with me on Linkedln: https://www.linkedin.com/in/bibek-shah-shankhar/
