What is the possibility of conducting ML experiments (especially in the area of biomedical Image Analysis)
without access to high-end Gpu or computing equipment and how can one overcome this challenge?
How can young researchers with a trained model deploy it into production?
What were your struggles if you faced any in building AI-based systems or starting out in the field of AI and how you overcame it.
How do you get to determine what real-life problem that will require an AI solution?
Fortunately, some large tech companies have begun to democratize access to compute and offer free cloud services. I would check out Google Colaboratory that offers free GPU/TPU access to train models.
There's no specific rule for determining what real-life problems can be solved with AI, if you have sufficient data and a solid understanding of what models can be used to solve the problem, then you can test it out!
At the end of each month, we give out cash prizes to 5 people with the best insights in the past month
as well as coupon points to 15 people who didn't make the top 5, but shared high-quality content.
The winners are NOT picked from the leaderboards/rankings, we choose winners based on the quality, originality
and insightfulness of their content.
Here are a few other things to know
1
Quality over Quantity — You stand a higher chance of winning by publishing a few really good insights across the entire month,
rather than a lot of low-quality, spammy posts.
2
Share original, authentic, and engaging content that clearly reflects your voice, thoughts, and opinions.
3
Avoid using AI to generate content—use it instead to correct grammar, improve flow, enhance structure, and boost clarity.
4
Explore audio content—high-quality audio insights can significantly boost your chances of standing out.
5
Use eye-catching cover images—if your content doesn't attract attention, it's less likely to be read or engaged with.
6
Share your content in your social circles to build engagement around it.
Contributor Rankings
The Contributor Rankings shows the Top 20 Contributors on TwoCents a monthly and all-time basis.
The all-time ranking is based on the Contributor Score, which is a measure of all the engagement and exposure a contributor's content receives.
The monthly score sums the score on all your insights in the past 30 days. The monthly and all-time scores are calcuated DIFFERENTLY.
This page also shows the top engagers on TwoCents — these are community members that have engaged the most with other user's content.
Contributor Score
Here is a list of metrics that are used to calcuate your contributor score, arranged from
the metric with the highest weighting, to the one with the lowest weighting.
4
Comments (excluding replies)
5
Upvotes
6
Views
1
Number of insights published
2
Subscriptions received
3
Tips received
Below is a list of badges on TwoCents and their designations.
Comments