Reflection

I recently took a probability course in school, and the experience left me thoroughly engaged and eager to reinforce my understanding of the material. As a result of this I have created a Jupyter notebook, which I have shared on GitHub. Within this notebook, I reviewed various concepts, including counting, axioms of probability, distributions of functions of random variables, moment generating functions, and more. Additionally, I provided illustrative examples and implemented some functions using Python.

Project Reflection

I came across a video on youtube that visualized all kinds of sorting algorithms. Here is the video. Instantly captivated by the concept, I felt compelled to embark on a similar project of my own. To get started, I followed a comprehensive tutorial on setting up a React application and proceeded to implement various sorting algorithms.

Introduction:

In this blog post, I delve into my project where I built a simple feedforward neural network from scratch, alongside a convolutional neural network (using pytorch). Neural networks have been widely recognized as powerful tools in machine learning, enabling us to tackle complex problems. As someone aspiring to enter the data science field, I was determined to gain a thorough understanding and practical experience in implementing these networks.

Reflection

In this project I craeted a python module that creates spotify playlists from the most popular songs in spotify playlists. It uses a some words to search the spotify api for playlists with same words in them. From those spotify playlists that we searched we go through each and find its most popular songs. Those most popular songs get added to the playlist we are trying to craete. Here is an example

link to the github repo