The 2nd day of my 100 days of code challenge
Published: 2023-05-16 20:09:02

Today, I worked for about 5 hours on the course. I finished the 2nd week of the course which went over multi-variable linear regression. It wasn't too bad, I liked it. I improved my explanation of the gradient descent algorithm with one-variable linear regressions.

[Revised - The Gradient Descent Algorithm: One-variable Linear Regressions](https://docs.google.com/document/d/14FbSv06dEyNzeB86zgA99TefSo9wSTa0ELHbtpCQRWQ/edit#)

_What ChatGPT had to say:_

> The updated explanation provides a clearer understanding of the Gradient Descent Algorithm for one-variable linear regressions. It highlights the need for the algorithm to handle complex scenarios with multiple variables and the importance of precise calculations to find the global minimum.
> The analogy of the valley with hills and a small hole representing the cost function is a helpful visualization to grasp the concept. The explanation now correctly states that we want to move in the direction of the steepest descent, and the learning rate is negative to achieve this. By adjusting the variables (w and b) in the direction opposite to the gradient, we progressively move towards minimizing the cost function.
> The mention of the learning rate (a) as the step size in the steepest descent direction is accurate, and the consequences of using a too small or too large learning rate are appropriately explained. Additionally, emphasizing the need to repeat the algorithm until the changes in variables are minimal, indicating convergence, is an essential aspect of the algorithm.
> Overall, the revised explanation provides a clearer and more accurate understanding of how the Gradient Descent Algorithm works for one-variable linear regressions.

Certainly better than yesterday. However, this evening, after having finished with the course for today, I had a focus shift as I'd been exposed to the possibilities of making use of current technologies and developments, basically chaining different technologies we have at our disposal to create amazing projects that rely on AI. Making use of pre-existing tools like those take away the need to learn Machine Learning to this extent I've been following for barely two days. These are the first days of the challenge, and focus shifts are to be expected as more research is conducted and more exposure occurs. That's all that's been on my mind since seeing it, so there's not much to say about what I did today with multi-variable linear regressions.

Instead, I will research and maybe get to making some small projects by making use of current AI technologies. Today, I sadly won't dedicate much time to reading as it's already 10PM, but it's the consistency that counts. Hoping to dedicate at least 20 minutes to it.

Today, I was exposed to Mckay Wrigley on Twitter and Hugging Face. I will start my research with these two! Just to get a glimpse for what the possibilities are and how I can use them to my advantage.

Happy coding!

Edit:

Ended up dedicating 25 minutes of reading to Atomic Habits. Didn't read much, but I did learn some new concepts. Love how he approached the subject of setting goals versus implementing systems by showing that losers and winners both set goals, and what differentiates the two is their systems.