The Most Tricky But Essential Mathematical Concepts Which Play Crucial Role In Machine Learning Algorithms.#1
This repository contains several educational purpose notebooks in which some of the most tricky but essential mathematical concepts that play crucial roles in machine learning algorithms, including Taylor Series, Constrained Optimization, Newton-Raphson Method, and Gradient Descent, are explained and programmed.
We do not need many things to get started with. As you can see, all the materials are built in the form of .ipynb(python notebooks), so we will have three options to be able to study the materials:
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Github: It is possible to study the files just by clicking on in the current page. However, you cannot change the codes and run the cells. In other words, they are available just read-only here.
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Google Colaboratory: Another option is to use the Google online service called Google Colaboratory, only by clicking on the button below. It is possible to run and apply changes to the cells.
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Jupyter Notebook(not recommended): The last option that we might have is to open these notebooks with your locally installed Jupyter notebook which I personally do not recommend.
I personally believe that it is not enough to deal with tricky and complicated problems. The final purpose of everyone should be to share what they learned so that others can make significant progress faster and achieve greater things that can even change the world. It does not matter who finally changes the world to a better place. We all are living in the same world, so we will eventually take advantage of the changes.
Fortunately, it is quite easy to find an infinite number of reliable sources supplying you with information. At the end, a list of sources that I found useful is provided.
- Coursera - Mathematics for machine learning - Imperial College London
- Deep Learning - the book written by Ian Goodfellow. Yoshua Bengio. Aaron Courville