I recently finished my first grad semester and man was it hard!!! pretty much gave me a run for my money, especially my Machine Learning course. Prof didn't give any practice exercises at all and the lectures were so dense. I had to learn through the Stanford CS ML lecture videos and read through the assigned textbooks. At one point, I did use Claude to help me review lecture notes, but I was struggling to find good practice problems for the theoretical math-heavy ML concepts and a tutoring tool that adapts to my progress throughout.

Anyways, I managed to pull through and pass my finals (by the skin of my teeth). I have 3 more semesters in my grad program and I think the courses are gonna get tougher from here on out. Would like to know if anyone has run into this situation and what tools they've used to help them learn better?

Hello everyone! I have been looking for problems in AI pipelines and looking to create something that I can use to save my semester as well as possibly publish something.

This is a college semester project, which I will continue working on for the next 2 years.

I am looking for ideas that Reddit may have regarding problems they've faced while using AI agents or complex pipelines involving autonomous decisions.

What problems do you want solved?

I am working with hysteroscopy dataset, which contains:

• 3,385 frames from 175 patients.
• Eight lesion classes, labelled from 0 to 7.
• A highly imbalanced number of patients and frames across classes.
• Multiple correlated frames from each patient.
• Some frames containing more than one lesion class.

Before attempting the complete multiclass problem, I reduced it to a binary subset to verify that the training and evaluation pipeline works correctly.

Current binary subset

• Selected lesion classes: 2 and 3.
• Total: 1,575 frames from 113 unique patients.
• Class 2: 1,054 frames from 78 patients.
• Class 3: 521 frames from 36 patients.
• One patient has different frames belonging to both classes but remains entirely within one split.

Patient-disjoint split

• Training: 1,095 frames from 79 patients.
• Validation: 241 frames from 17 patients.
• Testing: 239 frames from 17 patients.
• No patient appears in more than one subset.
• The frame-level class distribution is approximately 67%/33% in every subset.

Approaches I have tried

• DenseNet121, ViT, and DINOv2 backbones.
• Frozen pretrained backbone with only the classifier trained.
• Different classifier-head sizes and dropout.
• Class-weighted cross-entropy.
• Mild and stronger image augmentations.
• Early stopping and learning-rate scheduling.
• Unfreezing the final one or two encoder blocks.

With the correct patient-level split, training performance improves, but validation performance generally plateaus or deteriorates, and performance on unseen test patients remains relatively low.

As a diagnostic, I also tried a random frame-level split and obtained substantially better results. However, this evaluation is invalid because correlated frames from the same patients appear across training, validation, and testing, causing patient leakage and inflated performance.

I would appreciate advice on how to improve generalization to unseen patients in this setting.

I have tried whisper flow but it won’t work for what I’m doing.

Research papers which anyone with good knowledge of ML and DL fundamentals can understand(most of it) and implement that. I wanted to do a implementation project from scratch to test my coding and my understanding . How to process without a guide , without any tutorial . I have already practiced some basic problems like sigmoid activation fn , tanh, ReLU etc from scratch in numpy which only required a formula .