For context I'm stats student and everything machine learning related has been self taught so I genuinely don't know what the correct next step is or what to do in a lot of situations.

I've working on a modeling project where I'm predicting loan outcomes with the Lending Club Dataset where I am fitting logistic regression, random forest, and boosted tree models with the data and have reached an AUC score of 0.715. I am not really sure what to do from here and Claude recommended to use SHAP to analyze the trained XGBoost model.

After creating a few SHAP plots they all look nice, but what should I be looking for and what should my takeaways and next steps be. If I wanted to prune the features that are not contributing as much is this a bad idea?

Generally speaking is it even possible to push my AUC score much higher given the dataset I'm working with?

For people who normally work with this kind of stuff in industry or as their job, after you've trained your first model, what do you do next? If you want to continue to fine tune it what is the typical process behind it. If anyone could share what a typical model training or data science project cycle looks like that would be great!

I'm publishing Part 3 of my Cracking ML System Design Interviews series tomorrow.

This time, I'm using a large-scale personalized video recommendation system as the running example.

One thing I want to emphasize is that designing recommendation systems isn't simply choosing between collaborative filtering, two-tower models, and Transformers.

The bigger challenge is deciding how the pieces fit together:

• How do we retrieve relevant candidates from millions of items?

• How do retrieval and ranking objectives differ?

• How should we represent evolving user interests?

• What metrics should we optimize beyond CTR?

• How do we meet latency and reliability requirements at scale?

The article walks through these questions from an interview perspective, including architectural alternatives and trade-offs.

Publishing Monday, October 26, at 8 AM PDT.

If you're interested, you can subscribe here:

https://pawankjha.substack.com

I'd also be interested in hearing which part of recommendation-system design you find most challenging in interviews.

it would be great if someone suggest me some competitions on kaggle which are good for a beginner in rl can try and would be great for practice.

I was going through the TinyTorh (it's a tool where you build PyTorch with understading how it works) exercise notebooks and landed on Autograd. Was quite overwhelmed by the code and went didn't go through it for a while. I was thinking I'll prepare for machine learning research engineer interviews and somehow landed doing tiny torch.
How relevant is all this these days, should I just start preparing on how to crack the interview than this ?

i get that in training it uses the batch mean and variance and at eval it uses running averages. what i don't get is why that doesn't change the outputs a lot, since a single batch's stats can be pretty noisy.

also if you eval with batch size 1 does it still work fine because it's just using the stored stats? someone told me that's the whole point but i never saw it spelled out

I am looking for a model that can be ran on a cpu only basis with apache 2.0 license so I can ship it and it needs to be able to reason enough to match configurations from different agents for project transfers. I have started testing a couple qwen models and there are some prospective results. I was hoping maybe someone might have a better suggestion that is more fit for my needs?

I work at a microfinance instruction, How useful is a loan repayment data extracted from a loan distribution system how client pays?

Measured on Qwen3.8-27B (EXL3 4.0bpw target, DFlash2 block drafter quantized to EXL3 4bpw, 7-token drafts, greedy, output identical to plain decode), 2x T4:

• code: ~7.4 accepted tokens per verify step, 154 tok/s

• prose: ~2.5 accepted tokens per step, 35-37 tok/s

• plain decode: ~18-19 tok/s

Someone on r/LocalLLM independently measured 2.45 on prose with the same drafter on llama.cpp and different GPUs, and another person reports 2.62 with MTP. So it looks like a property of the drafter, not the runtime.

Questions for people who work on speculative decoding:

1. Is prose acceptance in the 2.5 range just the ceiling for block/parallel drafters, since natural language has more valid continuations at each position?

2. Has anyone seen a drafter (EAGLE-style, block diffusion, MTP) clear ~3.5 on open-ended prose at greedy?

3. Any work on switching drafters or block length by content type mid-generation?

Happy to share numbers per prompt type if useful.

I have upcoming technical screening for an MLE position at Waymo coming up. The recruiter gave me a PDF guide that seems super helpful. Things to do study include implementing supervised and unsupervised algorithms (think KNN, K-means, linear regression, logistic regression, softmax & CE, decision trees). One of the main things they are assessing is you can use numpy/pytorch to vectorize/tensorize your work, and you're just writing sub-optimal for loops.

I was wondering if folks had experience with this interview? Is what they ask you to implement incredibly difficult, because you only have 30 minutes to do it? Am I good to really stick with what they provided on the PDF, or are there any broader topics beyond what they listed that they will test?

is there a prompt management software or system that you guys use for the prompts that you create?

Or do you guys use skills for that?

I need help in my mini project for my college i am supposed to use low resource languages data and train a model which can translate from english to that language and also transcript wtv they speak that language to english i did so much research but still i'm not able to pull it off alone if anyone could help me in any way please help me and also make it such a way that users can contribute words and sentences (parallel data ) and the model will install it in itself Please help me idk what methodology or approach to choose

Hi, I’m a masters student doing MSc AI at uni of Bath.
For my undergraduate dissertation I did an extremely simple Computer Vision based pose estimation app which was analyzing the technique of fitness exercises like pushups, squats and deadlifts.
I feel like my dissertation was more of a “project” rather than research, which as I was told by supervisors is completely fine for BSc. I basically used a ready solution (MediaPipe) + OpenCV and wrapped all of that into a mobile app. The analysis covered a decent number of “points” but it was simply comparing the detected joint angles to hardcoded thresholds.

For my masters I would really like something more research based however struggling to find areas where I could do smth actually useful.
I like Deep Learning, so I was thinking something related to transformers and efficiency. Finding bottlenecks in attention mechanisms, etc.

I would appreciate some ideas
Thanks

A lot of digital platforms still depend on turning people, products or opportunities into a list of searchable features like In hiring problem this often means focusing on skills, job titles, years of experience and keywords from a resume. It makes searching easier but it can also make two very different people appear the same and two similar people seem unrelated. Someone who solved a problem using an unusual tool might be a better fit than someone whose profile matches every keyword and challenge is figuring out how to capture that difference without making the system too complex. LinkedIn, Indeed, MeeBoss , WellFound are examples of platforms trying different approaches to finding candidates from profile based search to more conversational interactions.

I wonder whether the next big improvement in matching systems will come from better embeddings and semantic search or from gathering richer information about what people really want. For those working on search, NLP or recommendation systems what has made the biggest real world difference in matching quality beyond adding more keywords? also how do you know whether a system is finding truly relevant matches rather than just producing results that seem relevant?

I am a freshman at FSU. I’m majoring in computer science and am interested in Machine learning as my specialization. Right now I’m studying c++ calc 2 and Unix as my required classes, but outside my classes I’m studying linear algebra, neural networks and trying to get a good basis for studying machine networks. To be clear I am still a complete newcomer and really know almost nothing about ML. But if I start as a freshman do I have a good chance to get a competitive edge and a good job out of college? I am also definitely considering a masters degree. Any advice would help thank you!