7. This should be simple, right? 3. A meeting could have been an email 13. CUDA out of memory 35. As per my last email… 9. The business need has… evolved 8. Just add ONE more feature before launch 5. Can we make it real-time? 38. Nice pajama top on the call 17. Forgot to kill my process… sorry team! 33. Restart kernel, run all, pray Free! 36. “Quick 5-min sync” = 45 minutes 31. Wait… which cell did I run last? 23. Dataset has 47 samples. Total. 40. Internet died at demo time 4. Requirements changed… again 24. The data is too noisy to use 25. Extreme class imbalance strikes again 1. Can we get 99.9% accuracy? 34. This notebook is 2000 cells long 14. The server is suspiciously slow today 37. I’ll document this later 27. Ground truth isn’t actually true 30. Forgot to shuffle the dataset 6. Stakeholder ghosted after week 2 32. Jupyter kernel died mysteriously 19. Server rebooted overnight. RIP my results. 29. Works perfectly on my machine 16. OOM killed at epoch 99 of 100 15. Someone hogged all GPUs 22. Who annotated this?! 20. We need more data 18. SSH connection died mid- experiment 2. Actually, let’s pivot the entire approach 12. Server crashed during the live demo 10. When will the model be production- ready? 21. The labels are completely wrong 26. Test data leaked into training set 11. Who left training running ALL weekend? 28. Loss went to NaN on step 1 39. Pet photobombed the video call 7. This should be simple, right? 3. A meeting could have been an email 13. CUDA out of memory 35. As per my last email… 9. The business need has… evolved 8. Just add ONE more feature before launch 5. Can we make it real-time? 38. Nice pajama top on the call 17. Forgot to kill my process… sorry team! 33. Restart kernel, run all, pray Free! 36. “Quick 5-min sync” = 45 minutes 31. Wait… which cell did I run last? 23. Dataset has 47 samples. Total. 40. Internet died at demo time 4. Requirements changed… again 24. The data is too noisy to use 25. Extreme class imbalance strikes again 1. Can we get 99.9% accuracy? 34. This notebook is 2000 cells long 14. The server is suspiciously slow today 37. I’ll document this later 27. Ground truth isn’t actually true 30. Forgot to shuffle the dataset 6. Stakeholder ghosted after week 2 32. Jupyter kernel died mysteriously 19. Server rebooted overnight. RIP my results. 29. Works perfectly on my machine 16. OOM killed at epoch 99 of 100 15. Someone hogged all GPUs 22. Who annotated this?! 20. We need more data 18. SSH connection died mid- experiment 2. Actually, let’s pivot the entire approach 12. Server crashed during the live demo 10. When will the model be production- ready? 21. The labels are completely wrong 26. Test data leaked into training set 11. Who left training running ALL weekend? 28. Loss went to NaN on step 1 39. Pet photobombed the video call
(Print)
7. This should be simple, right?
3. A meeting could have been an email
13. CUDA out of memory
35. As per my last email…
9. The business need has… evolved
8. Just add ONE more feature before launch
5. Can we make it real-time?
38. Nice pajama top on the call
17. Forgot to kill my process… sorry team!
33. Restart kernel, run all, pray
Free!
36. “Quick 5-min sync” = 45 minutes
31. Wait… which cell did I run last?
23. Dataset has 47 samples. Total.
40. Internet died at demo time
4. Requirements changed… again
24. The data is too noisy to use
25. Extreme class imbalance strikes again
1. Can we get 99.9% accuracy?
34. This notebook is 2000 cells long
14. The server is suspiciously slow today
37. I’ll document this later
27. Ground truth isn’t actually true
30. Forgot to shuffle the dataset
6. Stakeholder ghosted after week 2
32. Jupyter kernel died mysteriously
19. Server rebooted overnight. RIP my results.
29. Works perfectly on my machine
16. OOM killed at epoch 99 of 100
15. Someone hogged all GPUs
22. Who annotated this?!
20. We need more data
18. SSH connection died mid-experiment
2. Actually, let’s pivot the entire approach
12. Server crashed during the live demo
10. When will the model be production-ready?
21. The labels are completely wrong
26. Test data leaked into training set
11. Who left training running ALL weekend?
28. Loss went to NaN on step 1
39. Pet photobombed the video call
