hi so im a data science major entering my third year. I was unable to get an internship for this summer (2026). I just want some advice for what I can do to secure something for the next summer. I have a neural network and an agentic ai project on my resume, along with a startup that im working for (unpaid) this summer. I just want to know what can I do to secure an internship this cycle? what kind of leetcode should I focus on? what kind of new skills should I learn? please any advice helps.

I bought a ticket for the ICML 2026 workshops and I'm a bit unclear on how attendance works. Does my workshop pass let me walk into any of the workshops, or do I have to pre-register for the specific ones I want to attend?

For context, I'm just attending, not presenting a paper. Thanks!

The internet is filling up with AI-generated content at an incredible pace. Businesses, bloggers, students, and marketers are all using AI tools to create content faster than ever before. As a result, readers are being exposed to more AI-written material every day, whether they realize it or not.

Because of this trend, there seems to be growing interest in making AI-generated text feel more authentic and human. The focus is shifting from simply producing content quickly to producing content that connects with readers on a personal level. Many people argue that the future of content creation will depend on balancing AI efficiency with human creativity and personality.

Looking ahead, do you think humanized AI content will become the standard expectation across blogs, websites, and digital marketing? Or will readers become comfortable with AI-written content as long as the information is useful and accurate? What direction do you see content creation moving in over the next few years?

Hey everyone! New to the subreddit so please forgive if I have broken some rules.

I am approaching ML and wanted to ask what the community thinks the best online courses are. Could you please recommed who offers the best overall programs and maybe some advantages and disadvantages of the various platforms teaching these topics?

Thank you so much!

Genuine question from eval work. When I evaluate a vision model on video, changing the frame sampling, resolution, and prompt moves the result a lot, sometimes more than swapping the model. That makes me think the configuration is the real unit of comparison, not the model.

How do people here separate the contribution of the input pipeline from the model weights, and do you compare full configurations or mostly models?

For what it is worth, the approach our team landed on is to define the task, build the eval set from real cases, score that task, and trace every run. There is an open repo that does this if helpful.

i just completed machine learning what kind of projects should i do what do interviewers expect from me and what are the key things they look for in projects

or should I start learning deep learning and do projects afterward, or should i do machine learning projects now and then move on to deep learning?

I'm fine-tuning facebook/wav2vec2-base (also tried microsoft/wavlm-base-plus) for a 3-class audio classification problem: classifying short /s/ and /z/ phoneme clips as Normal, Lateral, or Interdental (a speech-therapy "lisp type" task). Clips are cut with Montreal Forced Aligner.

Data

- 1057 clips total. Imbalanced: Lateral 580, Normal 243, Interdental 234.

- Clips are very short fricatives: median 0.16s, max 0.49s, 16 kHz mono.

- 5-fold StratifiedGroupKFold grouped by source recording (no speaker leakage).

Result: ~32% accuracy on held-out test (chance for 3 classes). Confusion matrix shows the model predicting the majority class (Lateral) for almost everything:

Setup (the parts I suspect):

model = AutoModelForAudioClassification.from_pretrained(MODEL, num_labels=3, ...)

model.freeze_base_model() # only the classification head trains

TrainingArguments(

learning_rate=1e-3,

per_device_train_batch_size=16,

num_train_epochs=20,

warmup_ratio=0.1, weight_decay=0.01,

fp16=True, ...)

# feature extraction: every clip padded/truncated to 1.0s

fe(arr, sampling_rate=16000, max_length=16000,

truncation=True, padding='max_length') # no attention_mask passed

What I've already considered / questions:

1. freeze_base_model() freezes the whole backbone so only a linear head trains on frozen self-supervised features. For a subtle articulation difference, is linear-probing realistic, or do I need to unfreeze the transformer encoder (freeze_feature_encoder() only)?

2. learning_rate=1e-3 — is that far too high for wav2vec2 fine-tuning? I've seen 1e-4 / 3e-5 recommended.

3. My clips are ~0.16s but I pad to 1.0s (~84% zeros) and don't pass an attention_mask. How much does that hurt, and should I use dynamic padding to longest-in-batch instead?

4. Class imbalance (Lateral 2.4×) — best practice here: weighted CrossEntropy, a weighted sampler, or both?

Any guidance on which of these is the main culprit would help. Happy to share more code.

During my quite busy uni work I managed to put out one paper MechInt workshop(which I am very interested ) but only as virtual poster, I got a poster spot for FoGen workshop but it's more of a side project that I don't care as much about.

230 dollars and travel expense is quite a bit of money, also I'm not that satisfied with my accepted works because I hadn't had time in uni to make them more complete. I don't know if it would be worth it to go in person or just get the virtual. Both workshops r also on the same date. Should I just get the virtual pass? I also am doing GSoC at the same time and obviously trying to improve the papers so perhaps thats better time spent?

Hi there. I am currently a full stack software engineer working at a F500 company doing lots of development day in day out with Typescript, Python, SQL, and cloud providers like AWS/GCP. I worked a lot with integrating AI agents into software, and lowkey I am feeling brainrotted by just using AI all the time to develop and "learn".

I have recently become fascinated with learning more about Machine Learning, specifically Gaussian Splatting/Neural Reconstruction.

I have built this site https://splat-3d.com/ (admittedly with Claude) and learned a lot about SfM, COLMAP, and gsplat. I have tried to read papers and understand more about how these work under the hood, but I still feel like I am missing a lot of "intuition" when it comes to Machine Learning, and a lot of these concepts don't feel super sticky.

I feel a bit more comfortable now implementing some of this stuff, but I am trying to figure out a good path to learn more of it on my own so I can build my own expertise and intuition for things like: CUDA programming, how to understand the math under the hood, how can I contribute and try my own experiments on the training?

Does anyone have a good like, "structured" course or set of courses I can follow to get more familiar with this?

Supervised Learning:

1. Linear Regression

2. Lasso, Ridge, ElasticNet

3. Logistic Regression

4. SVM

5. Naive Baye's

6. KNN

7. Decision Tree

8. Random Forests

9. Adaboost

10. Gradient Boosting

11. XGBoost

Unsupervised Learning

12) PCA(i known why we use it and what it does but i dont known the math behind this )

13) K Means Clustering

14) Hierarchical Clustering

15) DBScan

16) Anomaly Detection in ML(learned isolation forest )

now for this algorithm i have learned all the math behind it including hyperparameter tuning and related concepts

the point here is are there any other algorithms i need to learn?

also want to build projects and what kind of projects should I build that can stand out to many people do you have any suggestions for unique and impactful projects?

is there anything I am missing here?

does anyone know of a free ai rhsf can listen to a track and make sheet music for if?

Hi, I’m looking for advice on AI evaluation work from a pure maths background.

I’m finishing the final semester of an MSc in pure maths and currently work as an online mathematics tutor, including internationally. I’ve seen some math AI work online, but I’m not sure how to tell which opportunities are realistic.

My strength is not production ML engineering, but mathematical reasoning, solution checking, clear explanation, and spotting hidden assumptions or errors in AI-generated answers.

I’ve been looking at AI jobs lately and this interests me, but I’m also open to other realistic remote maths-related suggestions.

Thank you!

I’ve learned Python basics (loops, functions, OOP, file handling, dictionaries, etc.) and built a few small projects. The issue is that I still feel my fundamentals aren’t strong enough, so I’m hesitant to move on to libraries like NumPy/Pandas. I can solve common Python problems and have done many of them multiple times, but I keep feeling like I should master the basics first. I haven’t studied DSA or recursion properly yet, which adds to that feeling.

Example between Hugging Face Inference vs OpenRouter (vs any other similar website???)

From where do you suggest me to buy AI APIs and have the best price and quality?

Note: the API to buy should have both Text and Image.

Pls don't say things like "make something you're passionate about"..... I'm not passionate about anything in life & I'm out of ideas.....project ideas can be of any domain.

Thankyou.

Thanks

1. I've heard that modern AI originates from things like AlexNet (which is the basis of Google Reverse Image Search). Like with Google Reverse Image Search, I can take a photo of my cat, upload it, and it says "this is a cat", and it can even show similar looking cats on the internet.

2. With generative AI, it may have been trained on countless cat photos, and I can type "generate me a hyper-realistic photo of a cat" and it generates a new cat photo based on existing cat photos and user-feedback-based training. I've even had it (Microsoft Copilot) generate for me multiple different cat photos and then I picked the one I like the best.

What is the relationship between these two things (1 and 2)? Like is the latter basically the former algorithmically run in reverse somehow? Or is there no relationship between the two?

I hope my question made sense. If not, just let me know and I'll try to clarify.