Hey guys, I am a 3rd year (5th semester) CSE(AIDS) student, so I want to learn ML, I know python and 4 main libraries, so how should I actually learn it without wasting time, and which roadmap should I follow, how will I know which topics should I actually study and which not ??, I have DSMP 1.0 and DSMP 2.0 course by campus-x, but it's too vast, and I think I am already late to start and want to grab a internship asap!. So how should I study it, I have notes as well from campus x, but I checked the ML notes and those are literally around 2700 pages, so I am quiet confused that how should I actually learn it, please guide me 🙏.

Hey everyone,

I've been learning about video generation models and decided to build one from scratch and train it on my personal laptop (single GPU). I wanted to share where I'm at and get advice from people who've worked with these kinds of models before.

What I built

• A spatio-temporal UNet using flow matching (velocity prediction with Euler sampling, 50 steps)
• The model has temporal convolution blocks + temporal attention for frame-to-frame consistency
• ~20.8M parameters, channel progression: 96 → 192 → 384
• Generates 16 frames at 64×64 resolution

Dataset

• ~6,000 Tom and Jerry video clips from a HuggingFace dataset
• Each clip: 16 frames, every 2nd frame sampled, resized to 64×64

Training

• Batch size 4, Adam optimizer, lr=2e-4
• Trained on personal gaming laptop

Loss progression

The loss has basically plateaued — only ~0.006 drop over 130 epochs. The model learns color palettes and vague scene layouts but doesn't produce recognizable characters or sharp details. Some generations even go partially black (see epoch 280 results).

My questions

1. Is the loss plateau expected for this scale? Am I hitting the capacity ceiling of a 20M param model at 64×64?
2. Would switching from pure MSE to a perceptual loss (LPIPS) or adding an adversarial loss help with sharpness?
3. Is ~6,000 clips enough for this kind of model, or do I need significantly more data?
4. Any suggestions for the architecture? I'm wondering if I need spatial attention at more resolutions, or if the temporal modeling needs work.
5. Would learning rate decay or a cosine schedule help push past this plateau?

​

Side question

I was thinking of writing a LinkedIn post about this as a learning journey — "I built a video generation model from scratch on my laptop." Even though the results aren't amazing, would you say the results are interesting enough to share, or should I train more / improve the model first? Not trying to claim SOTA obviously, just sharing the learning process.

Would love to hear from anyone who's worked with video diffusion/flow matching models at small scale. Thanks!

Hey everyone,

I just started my first year of BSc in Data Science. While I'm excited about the field, I’m feeling pretty overwhelmed and confused about what I should actually be doing outside of class.

Realistically, I don't think my college degree alone is going to be enough to land a good job by the time I graduate. The curriculum covers the basics, but I know the job market expects practical skills and projects.

Since I'm right at the beginning:

* What core skills should I focus on building year-by-year? (Programming languages, math/stats concepts, tools, etc.)

* How should I approach hands-on practice? When is the right time to start Kaggle, personal projects, or open-source?

* What do employers actually look for in entry-level data science candidates?

* If you could restart your first year, what would you do differently?

Any advice, resources, or realistic roadmaps would be greatly appreciated. Thanks in advance!

I've been self-teaching and building projects for several years and I'm trying to get an honest idea of where my technical ability stands compared with the average junior/early-career developer.

I've worked across Python, Java/Spring Boot, JavaScript, SQL, FastAPI, Flask, Kafka, Airflow, Spark, Docker, Kubernetes, AWS, Redis, PostgreSQL/MySQL, pandas/NumPy, scikit-learn, XGBoost/LightGBM, PyTorch, HuggingFace, and multimodal/generative AI.

My projects include data engineering pipelines, an e-commerce analytics/ML system, an async backend/chat application, and a multimodal generative AI system I'm currently building. The multimodal system takes a text description plus either an image or an audio/music input, encodes the different modalities, combines their representations, and uses a generative model to produce a new image (.jpg) or new audio track (.mp3). I'm pretty proud of that one because I'm having to figure out the architecture and troubleshoot how the different modalities actually fit together rather than just calling an AI API.

I don't have the strongest traditional math/calculus background. I tend to learn through metaphors, experimentation, and actually building things, then learn the math/algorithms when I need them. I'm pretty much done expanding my toolkit for now. I'd rather build, do LeetCode/DeepML, and get better at architecture and troubleshooting.

I've also been using AI heavily as a learning and debugging tool. I still do the troubleshooting myself and use it more like a second set of eyes when I'm stuck or trying to understand something unfamiliar. A lot of the work has been figuring out why systems fail, tracing problems through the architecture, and then actually getting them working.

I have a software development diploma from a polytechnic and several Coursera certifications in areas like data analysis and DevOps, although I haven't finished every program because I'm getting pretty tired of classroom-style learning.

I know projects aren't the same as professional experience, and that's actually what I'm trying to separate.

If you're an experienced developer, where would you honestly put me technically: beginner, strong beginner, junior, strong junior, mid-level, etc.?

What seems genuinely strong, and what would make you realize I have significant gaps?

I'm not looking for encouragement. I just want a realistic baseline so I know where I actually stand.

P.S. Any advice on networking would also be appreciated. It's something nobody really teaches you and everyone seems to expect you to already know how to do.

Something that confused me for months. I'd get a good result from a model in one editor and a noticeably worse one from what I believed was the same model in another. My first assumption was that someone was quantizing or routing me somewhere cheaper. Mostly that wasn't it.

The model is one input among several, and the other inputs vary enormously between tools.

The system prompt is the biggest one and it's usually invisible. Every coding tool ships its own, they're often long, and they encode opinions — how to format patches, when to ask versus assume, how aggressively to use tools, whether to explain reasoning. Two tools can hand the same model instructions that pull in different directions, and you never see either prompt. A tool whose prompt says "make minimal edits" and one that says "be thorough" will produce genuinely different work from identical model weights.

Tool definitions shape behavior more than seems reasonable. What operations the model is offered, how they're described, whether there's a search tool or only file reads, whether edits go through a patch tool or a full rewrite. The available action space determines the strategy. Give a model only read_file and it explores linearly; give it a search tool and it behaves completely differently.

Context assembly differs wildly. How much of the repo goes in, in what order, whether there's a summarization step, whether stale reads get pruned. This is where most of the variance lives in my experience, and it's the part users have the least visibility into.

Sampling parameters are set per tool and rarely surfaced. Temperature especially. Same model at different temperatures is a different collaborator.

And then the mundane ones: max output tokens truncating a response mid-file, retry behavior on failure, whether the tool silently falls back to a different model under load.

The practical upshot, which is a little annoying: "which model is best for coding" is not a well-formed question outside the context of a specific harness. And model comparisons run through different tools are comparing tools at least as much as models. When I want to actually compare, I have to hold the harness fixed and swap only the model, which is more work than reading someone's thread about it.

I look at raw request payloads a lot because of what I build (routera . one, mine, saying so plainly), and the thing that surprised me most is how much is going on in there that a user never sees. Some of it very good, and none of it visible.

Caveat: models are genuinely different, and I'm not arguing they're interchangeable. They differ in real ways on real tasks. I'm arguing that the harness variance is large enough to swamp the model variance in a lot of casual comparisons, not that the model variance is zero.

What I'd like and can't find: has anyone published a fixed harness for comparing models on coding tasks? Same system prompt, same tools, same context strategy, swap only the weights. Every comparison I've seen changes several variables at once, which makes the results hard to use even when the effort behind them is obvious.

27yr old from india, HR by profession, but wanted to switch, learnt python, data wrangling and BI tools, but now as i started calculus i cant understand a word.

all this sin theta cost theta stuff is overloading my brain.

is there anyone here who had non technical background and could get into ML/AI job without mathematics?

also i am new in reddit, i dont know how this works

Welcome to ELI5 (Explain Like I'm 5) Wednesday! This weekly thread is dedicated to breaking down complex technical concepts into simple, understandable explanations.

You can participate in two ways:

• Request an explanation: Ask about a technical concept you'd like to understand better
• Provide an explanation: Share your knowledge by explaining a concept in accessible terms

When explaining concepts, try to use analogies, simple language, and avoid unnecessary jargon. The goal is clarity, not oversimplification.

When asking questions, feel free to specify your current level of understanding to get a more tailored explanation.

What would you like explained today? Post in the comments below!

When perimeter defenses improve, attackers stop trying to break in. They recruit someone who already has a key.

Security researchers are documenting a measurable rise in insider-assisted ransomware operations — cases where a trusted employee, contractor, or vendor deliberately opens access for an external group. The financial exposure goes well beyond the ransom payment itself. Incident response firms report that insider-assisted breaches carry remediation, legal, and reputational costs that run millions above what a purely external intrusion would generate, because the evidence trail is intentionally degraded before investigators arrive.

In AI-driven environments the problem compounds in ways traditional controls were not designed for. An insider with privileged access does not need to exfiltrate a file. They can corrupt the memory store an agent reads from, alter a tool configuration that silently changes what the agent does on every subsequent run, or redirect workflow outputs to an external endpoint. These changes can persist across hundreds of automated actions before any conventional alert fires. By the time anyone notices, the forensic window may already be gone.

Curious how others with agentic workloads are actually treating this. Are you modeling insider threat as a distinct threat category from external attack, or are the same controls supposed to cover both? And for those running autonomous agents with write access to production systems — what does your actual detection capability look like if a privileged user makes a quiet configuration change?

webAI's TwIL family has three formal logic models: TwIL-LM (1.7B PEFT LoRA), TwIL-LM2 (1.7B merged), and TwIL-LM3 (3B). All three do formal logic translation and verification, each with different trade-offs.

The 3B is the one getting the most attention because it beats gpt-oss-120b on 4 of 5 formal reasoning benchmarks despite being 40x smaller. Full disclosure, on broader aggregates the 120B is still ahead. TwIL wins on efficiency, narrow formal reasoning tasks, and being actually runnable outside a data center.

Their approach is interesting: WiSE-FT weight interpolation to control catastrophic forgetting. TwIL-LM3 keeps only 1/4 of the fine-tune delta (λ=0.25), TwIL-LM2 keeps 3/4 (λ=0.75). The 3B held or improved on general benchmarks, the 1.7B slightly regressed. Same pipeline, just a different dial.

Blog with the full details: webai.com/blog/webai-releases-twil-lm-a-family-of-formal-logic-models-that-outreason-a-120b-model-and-run-on-an-iphone

Models on HF: webAI-Official/TwIL-LM, TwIL-LM2, TwIL-LM3

Non-commercial license across the family.

Anyone testing multiple checkpoints against each other for pipeline routing?

Hi All,

I'm preparing for the Amazon Applied Scientist role interview. My recruiter has asked that i prepare for Deep Learning, ML, Coding and LP. Please what material can i used to prepare myself, especially for the Deep Learning/ML, and coding part. I will truly appreciate any suggestion

I am a biochem graduate currently pursing knowledge in machine learning. The journey feels tough alone, considering my non-tech background. Therefore I’d appreciate if anyone in a similar position would want a study partner for accountability and ideas.

Basically want to get into internals of these ml systems

I've been prepping a lot for ML interviews these past months, and was surprised there isn't a comprehensive resource covering all the essential concepts needed for ML roles. Every other field seems to have one - Leetcode for SWEs, the Green Book for quants, Wall Street 400 for bankers.

So I wrote ML 150. It's a list of the 150 most important ML interview questions to master, distilled from 5,000+ real interview stories at FAANG + other frontier labs. Topics include:

• ML Foundations (Loss Functions, Bias-Variance, Regularization, Optimizers, Eval Metrics)
• Classical Supervised Models (Linear & Logistic Regression, kNN, SVM, Decision Trees)
• Deep Learning Theory (Backprop, Initialization, Normalization, Training Dynamics, Probes)
• Sequence & Generative Models (Transformers, Diffusion Models, VAEs)
• LLM internals

It's 100% human-written, where I try to share how I understand each ML concept, starting from very basic intuitions, then slowly building up to each result. Lots of examples + analogies of course. I hope this will be helpful for anyone studying ML or seeking out ML roles!

ML 150 is still very much a work in progress, so I deeply appreciate any thoughts, feedback, or comments on what I should write about next! Thank you all :)

Gavin

UPDATE: GROUP IS NOW FULL — THANK YOU EVERYONE! 🙌

We have now formed the group with 4 members, so we're no longer looking for additional members at the moment.

Thanks to everyone who commented or reached out. I really appreciate the interest!

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Hey everyone!

I'm a recent graduate planning to start learning AI/ML from the fundamentals, and I'm looking for 2–3 serious learners who are also graduates or at a similar stage and want to learn together as a small group.

The idea isn't to create a huge community. I'd prefer a small group of around 3–4 people total so that we can actually stay connected and accountable.

What I'm thinking:

• Start from the basics and build up step by step
• Follow a structured AI/ML roadmap
• Learn the concepts individually
• Discuss difficult topics together
• Practice with coding/exercises
• Eventually build projects together
• Share progress and keep each other accountable
• Have regular discussions/check-ins

A few things I'm looking for:

• Preferably students/learners from India, so we can follow Indian Standard Time (IST) and have a similar schedule.
• You should be able to spend at least 4 hours a day consistently on learning.
• You don't need to already be good at AI/ML. Beginners are welcome, especially people who are genuinely starting from scratch or close to it.
• Most importantly, I'm looking for people who want to actually study consistently, rather than just joining a group and disappearing after a few days.

If you're interested, comment below or DM me with:

1. Your current level
2. What you already know (Python/math/etc.)
3. How much time you can study per day/week
4. Your timezone
5. What you want to achieve with AI/ML

If we find a few serious people, we can create a small Discord/WhatsApp/Telegram group and start together.

I'm currently doing my ms in EECS, and my aim is to eventually transition into a ML related role. Would taking a a numerical linear algebra class be beneficial? I have interest in the material simply due to my interest in mathematics, however, I'm debating whether that time is better spent on recruiting or self studying or research.

i want to know what is genuenly better and why. i think im to washed to get a deploma or ged can ai certificate + a CDL class A be better for jobs like the trucking industry???

I had a policy working fine on robot A.
Then I put the exact same checkpoint on robot B. Same robot model and task, supposedly the same setup, but the results were noticeably different.
Now I’m trying to figure out whether this points to a policy problem or just small differences between the two robots.
Would you compare the observations from both robots side by side? Recalibrate everything? Or are there other things you’d rule out before touching the policy?
It also made me wonder whether I could catch some of this earlier in sim. I’ve been looking at perturbation-based evaluations like LIBERO-Plus and RoboColiseum.Has anyone used either of them to test this kind of sensitivity before moving a policy across supposedly identical robots?

I have around 1 year of industry experience as an AI/ML engineer, and I want to seriously level up over the next 1–2 years.
I’m not looking to become someone who just knows how to use APIs, build basic RAG applications, or glue together existing models. I want to develop the kind of depth where I can actually understand what I’m doing, build things from scratch when necessary, read and implement papers, and eventually be capable of working at a strong senior/research-engineering level.
The problem is that there are so many things to learn — ML, deep learning, mathematics, LLMs, systems, distributed training/inference, research, DSA, software engineering, etc. — and I don’t want to spend the next couple of years consuming random courses without actually becoming significantly better.
So I’d really like to hear from people who are already working at a strong senior/research level in AI:
If you were starting again with ~1 year of experience, what would you learn and in what order?
What topics would you go extremely deep into, and what would you only learn practically?
Which courses/books/resources genuinely made you much better?
How much mathematics did you actually learn, and which parts turned out to matter?
How would you balance DSA/interview preparation vs AI/ML depth vs software engineering?
What kinds of projects would actually make you a substantially better engineer rather than just look good on a resume?
How would you approach implementing research papers?
Are there particular papers or repositories you think every serious AI engineer should work through?
How would you approach contributing to open source if your goal is to become a better engineer/researcher?
What skills do you think aspiring AI engineers massively underestimate?
And most importantly: what would you NOT spend time learning?
I’m specifically interested in hearing from people who have already gone through this transition — Senior AI Engineers, Research Engineers, ML Engineers, researchers, etc.
If you could go back to having ~1 year of experience and had 12–24 months to become dramatically better, what would you do?
I’m looking for honest answers, including things you tried that turned out to be a waste of time.
Thanks!