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Now, find your favorite topics in one place and learn in depth!

Photo by Omid Armin on Unsplash (Modified by author)

Hi again! I’m happy to say that I’ve started to write series of articles that cover the most important topics in depth. For your convenience, I’ve arranged them under a few different categories. I invite you to start exploring them!

1. Neural Networks and Deep Learning Course

Learn from the basics with a hands-on approach by using…

Reduce the size of your dataset while keeping as much of the variation as possible

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In both Statistics and Machine Learning, the number of attributes, features or input variables of a dataset is referred to as its dimensionality. For example, let’s take a very simple dataset containing 2 attributes called Height and Weight. This is a 2-dimensional dataset and any observation of this dataset can…

Choose the Right Laptop for Data Science and Machine Learning

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If you’re learning Data Science and Machine Learning, you definitely need a laptop. This is because you need to write and run your own code to get hands-on experience. When you also consider portability, the laptop is the best option instead of a desktop.

A traditional laptop may not be…

Neural Networks and Deep Learning Course: Part 7

Image by author, made with draw.io and matplotlib

Technically, we can consider logistic regression as a very simple neural network model with no hidden layers. It has just the input layer and the output layer. The output layer has only one node as the logistic regression performs binary classification.

Let me explain this further.

What is a logistic regression model?

Logistic regression is a…

Let me explain this using an example — Neural Networks and Deep Learning Course: Part 6

Image by author, made with draw.io

In Part 5, we’ve discussed different types of activation functions and their uses in neural networks.

So, “What happens if you do not use any activation function in a neural network’s hidden layer(s)?”. We’ll explain this using the following example.

Consider the following neural network model with two hidden layers.

Analyzing different types of activation functions with visual representations — Neural Networks and Deep Learning Course: Part 5

Image by author, made with draw.io and matplotlib

Introduction

In Part 1 of our Neural Networks and Deep Learning Course as introduced here, we’ve discussed the main purpose of using activation functions in neural network models.

Activation functions are applied to the weighted sum of inputs called z (here the input can be raw data or the output of…

Rukshan Pramoditha

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