What is your heuristic to select the number of samples for a classification problem?

Folks, looking for some constructive feedback on my resume. Please look from the angle of technologies I've worked on, their depth and the impact highlighted from the resume. What should I focus on more? Thanks for having a look.

1. Early cut off looking at training_loss 2. Early cut off looking at validation_loss 3. Set Higher Regularization? Increasing data is not an option, have used SMOTE related technqiues for interpolating more data for the imbalanced classes already. Just trying to understand what do other folks do.

Hey everyone, happy to answer any questions around Data Science/ML/AI and making a career within it A brief about me, I work currently as a Sr. ML Engineer at an Indian unicorn startup I graduated 5+ years ago and have since worked as a Data Scientist / ML Engineer at an ecommerce company and social media company. My designations have changed based on the industry requirements and work too changed based on that. Would love to take questions on what it means to be an ML Engineer, my early learnings from a career in data science, and thoughts on AI. Look forward to all questions :)

Has anyone worked with ML problems where we need to match N elements from 1 dataset to M elements from another (N>M, or 1 m can have multiple n)? What business domains have such problems? What modelling approaches, feature engineering and ML models did you use?

I have 3 years of experience in other domain and i am trying to get into Data science. Any suggestions amid these layoffs ?

- What's your codeforces rank? - Why do / don't you do competitive programming? - What's your take on the impact AI models like AlphaGo will have on the sport?

Is anyone here who has built Indic-focused SLM/LLM models for text generation? If yes, please DM me. Want to understand the following things 1. Want to understand the training/fine-tuning and running costs. 2. I also want to understand how much training data was required to attain minimum acceptance criteria. 3. Was the effort worth the reward? Meaning that had you not built the model yourself and used any other foundational models, how could the cost and output differed?

When the IPO for the retail category is over-subscribe let's say by 2x time, everyone seems to say that the probability of allocation is 1:2 i.e 50%, but is that the correct way of calculating probability? Different retail investors apply for different numbers of lots, maximum allottees = (shares offered / total shares in 1 lot), it is possible that number of applicants are less than maximum allottees if maximum applicants are applying for more than 1 lot, in such cases if IPO is 2x times subscribed by total applicants less than max allottees then everyone is 100% going to get at least 1 lot, so probability becomes 100% and not 50%. Is this calculation correct? What i'm missing or misunderstood? I have watched many videos and read many articles but i'm still not clear on how probability calculation should be done correctly.