After moving into a new apartment, my wife and I wanted to fill the spaces on our empty white walls. You may not know my wife and me, but naturally, we agreed on having more of our dogs on the wall. We had two 12" by 16" sized frames lying around, and my wife proposed we print out pictures of our beloved companions. In our defense, they are incredibly cute.
Question: A couple has two children, what is the probability that both children are girls given that one of the children is a girl?
Hint, the answer is not 50%. In this article, I will frame this question from a different perspective. I will begin by supplying a short introduction to the human genetic code and how it’s stored. Then, I’ll answer the question with and without using the conditional probability formula.
Each cell in our body contains a big recipe book that holds the recipes to almost every protein the body needs to survive (and more). We call this…
In this blog post, I‘m going to provide an example of one of the use cases for deep learning — image classification.
Before describing my image classification project, I’ll give some biological background and introduce convolutional neural networks (CNN). In the next post, I will provide a more in-depth explanation about the mathematical equations that operate behind the scenes.
In introduction to deep learning (part 1), I explained how deep learning (DL) is a subset of machine learning (ML) and that ML is a subset of artificial intelligence (AI).
Also, I described the history of neural networks (NN), what deep…
As part of my Flatiron experience, I was paired up with a partner and tasked to perform multiple linear regression, interpret the results and present the findings. In this blog, I am going to present, and criticize the steps we’ve taken along the way to complete the project. We divided the work into 5 steps:
The dataset we picked was collected by the World Health Organization (WHO) and the United Nations by Deeksha Russell and Duan Wang. In this project, we performed multiple…
In this article, I’ll try to explain what deep learning is and how it relates to machine learning.
Even though there’s a lot of sources discussing this subject online, I couldn’t point to one source that was both exhaustive and concise.
Before I begin explaining what deep learning is exactly, I’ll first explain a little about artificial intelligence, machine learning, neural networks, and the relationship between these topics.
This article is meant to provide you with a background on the topic while not going too in-depth. …
As a beginner data scientist, I often find myself staring at my results thinking about what would be the best way to present them. The most common methods to visually illustrate relationships in our data are graphs and tables. Graphs’ main purpose is to present data that is too complicated to describe in words. While tables are best used when you need to compare individual results or show precise values. The four basic presentation types for graphs are “comparison”, “composition”, “distribution” and “relationship”. …
“Bioinformatics is the science of collecting and analyzing complex biological data”
Bioinformatics combines elements of biology and computer science, using computational methods to analyze biological data. Since 1953 and the discovery of DNA the field of molecular biology has experienced a steep rise in the amounts of data to process. Scientists have been gathering large amounts of data of genome sequences from many species. Nowadays data analyses in bioinformatics predominately focus on large data sets such as macromolecular structures, genome sequences, etc…
Today I would like to discuss the map function in Python. How to use it, why I usually prefer using it instead of a regular For loops, and introducing list comprehensions.
To make sure I get my point across I will cover a few basics first. Hopefully, this post will also be approachable to people with little Python experience.
Iterables can be different types of ordered lists, in general, we call those lists that contain information in a set order — “Arrays”.
The map function executes a specified function for each item in an array.