https://github.com/apiad/threads
Links to my best (IMO) Twitter content
https://github.com/apiad/threads
Last synced: 8 days ago
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Links to my best (IMO) Twitter content
- Host: GitHub
- URL: https://github.com/apiad/threads
- Owner: apiad
- License: cc0-1.0
- Created: 2022-07-24T10:55:43.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-07-24T15:26:54.000Z (over 3 years ago)
- Last Synced: 2025-02-21T15:51:14.481Z (12 months ago)
- Homepage: https://apiad.net/threads
- Size: 40 KB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# My best Twitter content
[](https://twitter.com/alepiad)
This is a list of links to some of my best threads. Threads are grouped based on theme and sorted more or less by how I think they should be read.
## Intro to Machine Learning
A list of short threads introducing the most relevant concepts in machine learning.
- What is ML?
- Types of machine learning problems.
- Parametric vs Non-parametric models.
- Generative vs Discriminative models.
- Choosing the right metric.
## Mindblowing ideas
On select Mondays I write threads about some of the greatest ideas in AI and computer science in general.
- Language models are one of the hotest topic in machine learning and particularly in NLP.
- Generative Adversarial Networks are one of the coolest generative models, especially well-suited for image synthesis.
## Techy stuff
On select Tuesdays I write about technical stuff, tools, frameworks, and apps that you can use right away.
- AutoML is a practical approach to the progressive automation of the most boring machine learning tasks.
- A list of 10 AutoML tools that you can use today.
## Wisdom
On select Wednesdays I write threads with advice for communication, career progress, and being a nice human being in general.
- Presenting an idea with the Why-What-How framework.
- The Vision-Steps-News-Contribution framework for presenting ideas.
- Three lessons on the interaction between academia and the industry when creating ML-based solutions.
- Tips to improve your technical writing skills that don't require talent or any source of divine inspiration.
- Three health habits for grad school that helped me survive my PhD with minimum psicological damage.
- My process for making a literature review that doesn't suck and doesn't bore you to death.
## Theoretical topics
On select Thursdays I write intuitive explanations for theoretical topics in Computer Science.
- Algorithmic complexity allows us to compare algorithms regardless of the tech stack in which they run.
- NP-Completenes is the study of the most complex problems in Computer Science.
- P vs NP is the question of whether all problems are as easy to solve as to validate.
- The halting problem is the most famous undecidable problem in Computer Science.
## Philosophy of computer science
On select Fridays I write threads about philosophical topics in Computer Science.
- What is intelligence? This is Alan Turing's idea.
- Are we living in a simulation?
- Can we answer all questions?
- How do you know that what you know is true?
- The ugly duckling theorem says there's no natural way to cluster objects.
- Self-driving cars and the trolley problem.
- Would a superintelligence be nice with us?
- Mary and the nature of qualia.
- Consciousness and your sense of self.
## Rants
Random stuff I've written on several topics.
- Twitter is not a free University, but there's still value you can get from it.
- Github Copilot, is it worth it?
- Recursion, what it is and why is it important.