Why Write A Blog?

I've just been accepted to follow the 2018 version of Deep learning - part 2 on fast.ai, and I'm pretty excited about it. As I'm reaching the stage at which this is becoming more than a hobby and the plan is to switch careers to Data Science, I've taken the time to ponder all the alternatives and I've settled on self-studying.

Which brings me to this blog. How do you measure your progress when there's no one to grade your work? One solution I've found is to write about what I learn. Nothing here is going to be new, I simply intend to explain in my own words concepts that have been detailed elsewhere (probably with fewer grammatical mistakes!). I could say that my only reader is going to be my mom, but she doesn't read English, so that won't even be the case. As an ex-teacher, I simply believe you've never completely mastered something until you've taught it to someone else.

For now the curriculum I have settled on is:

• the deep learning course of fast.ai (part 1 and 2);
• the machine learning course of fast.ai;
• the online book of Michael Nielsen;
• Python for data analysis.

I will update this list as it grows. I will try to get to the bottom of all the concepts I learn, and I intend to code everything from scratch. Since the fast.ai library is wrapped on top of pytorch, this is the library I will mostly use, along with numpy and pandas. All these articles will be in the Deep learning category.

I also plan to play along with different approaches and parameters, to highlight the importance of each decision we make when building a model. To that end, I'll design and implement as many experiments as I can and put the results in the Experiments category.

Explaining things is all very good, but it's even better to show what you can actually do. I plan to enter a few Kaggle competitions, hopefully achieve a good ranking, and I will also use some of the articles of this blog as a portfolio to demonstrate my skills. Those articles will be in the Portfolio category.