Discover more from Infinite Waves
Issue #4 of the
Infinite Waves
Newsletter
In this post I’ll be talking about my experience so far of exploring AI.
One of the main reasons for this newsletter is for me to learn more about the Artificial Intelligence world as a whole.
By day I’m a web developer, and the aim is to move more towards AI work and projects. I want to get a better appreciation for what’s currently out there, and what might be coming next, in order to build knowledge and understanding, whilst working on related practical skills.
The hope is that the newsletter might also serve as a useful overview or gateway for other developers that are wanting to do the same.
For now the topics are pretty open, but I think over time the content will start to galvanise around distinct thematic streams.
I’ve been following the Indie Hacker’s community for a while and it’s very much in the spirit of what that’s all about. Indie developers bootstrapping their way to online products and services.
Python 🐍
I’m starting to learn Python (again). Over the years I’ve attempted to learn Python a number of times using a mixture of online courses and resources, including the official documentation, Udemy, SoloLearn (a great app I’d highly recommend), the online book ‘Learn Python the Hard Way’, and YouTube (obviously).
I think the reason I’ve never stuck with it was because I never had a clear goal or project, I was just learning it for the sake of learning a general programming language. I was also more focused on web development.
Now, however, I’ve got a concrete purpose and incentive with AI projets. The way I’m going about it is firstly by completing the micro-courses on Kaggle. Starting with Python and then I’ll move on to their machine learning courses.
It was a learning path suggested by Santiago Víquez in his article “If I had to start learning Data Science again, how would I do it?”.
I’ve also got the physical book ‘Practical Programming: An Introduction to Computer Science using Python 3’ (Gries, Campbell, and Montojo). I keep it on my desk with a notebook and pen so I get in the habit of opening it and reading daily. As James Clear states in his book ‘Atomic Habits’, to build a habit effectively make it obvious, attractive, easy, and satisfying.
GitHub
I’ve set up an Infinite Waves GitHub account so I can start committing some small projects as I learn.
Learning about AI
For the AI/ML side one great resource I use a lot is Lex Fridman’s podcast. Lex has credentials as an MIT researcher and now interviews leading scientists, academics, entrepreneurs, and engineers in a variety of science and technology fields, with an emphasis on AI. Because of his first hand research experience he’s able to discuss topics at length and in detail, whilst asking astute questions that really extracts specialist knowledge from his guests. This podcast is great to get a diverse, yet sophisticated, appreciation of the cutting edge in AI research.
One tip I came across recently, if you want to learn something more efficiently, is to read highly-regarded textbooks. It’s something that’s easily overlooked when so many apps, blogs, podcasts, YouTube channels, and popular books are readily available.
With this in mind, I’ve just started to read the textbook ‘Pattern Recognition and Machine Learning’ by Christopher M. Bishop.
It makes sense to go direct to the source where a lot of the information is coming from anyway. Not only is the content more comprehensive but it’s not had any interpretation or filtering applied to it.
What’s next for the newsletter?
Starting from next week I’ll be increasing the frequency to two issues per week. On Monday it will be a focus on highlights, news, and points of interest in AI from the previous week. A kind of snapshot of AI-related content.
Then Thursday’s newsletter will largely focus on a central theme, technology, company, or subject matter and be more in-depth and detailed than Monday’s.
If you got some value from this content it would be a great token of support if you shared it to your peoples:
Comments are open if anyone has anything they’d like to say or ask.
List of resources:
“If I had to start learning Data Science again, how would I do it?”
Practical Programming: An Introduction to Computer Science using Python 3
I have used Python quite a lot for implementing automated flight control of aircraft in the X-Plane flight simulator. In your "definition #2" article, you mention "Computer Algorithms." Is this referring to Code you would obtain from somewhere else? Are there "object oriented" facilities in Python that I'm not aware of? Or would you write code yourself which uses algorithms found in textbooks? I would prefer the latter, having never learned OO methods. (Even though my experience goes back to 1979, with Assembly, Pascal, BASIC, many versions of C, Fortran, Lua .. etc.)
Hey Daniel,
Your journey diving into AI from a web dev background is inspiring! It’s refreshing to see someone charting their course in this vast landscape of AI, aiming not just to learn but to share the ride with others. Your newsletter's pivot toward AI is a goldmine for fellow devs eyeing a similar shift.
Python, the gateway drug to AI, seems to have found its purpose with your foray into micro-courses on Kaggle. Love the deliberate habit-building with the physical book and notebook – James Clear would approve! Lex Fridman’s podcast and Bishop’s textbook are stellar choices for deepening your understanding.
The newsletter's new schedule sounds awesome, offering both snapshots and deep dives into AI. Thanks for sharing your resources and plans; it's a treasure trove for all on this path. Looking forward to more insights!
Thanks a ton for your efforts and for being an inadvertent guide on this AI trail. Keep rocking, Daniel!
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