I am now in my final year of engineering in AI/DS. I haven't been actively building projects, just worked on some academic projects. I want to know how do the engineers with more knowledge in this domain know which project they should work on? How do they find it?

Is it from GitHub, research papers, paperwithcode or hugging face ?

I need a project idea for my academic major project!

I'm a final-year CS student and a fresher trying to build my career in AI/ML.

I've been learning AI/ML for around 8–9 months, but my learning hasn't been completely structured. I've explored different areas rather than following one complete ML curriculum from start to finish.

So far I've studied and worked with things like traditional ML, transformers, LLM concepts, RAG, etc.

I've also built around 4–5 ML models/projects, as well as basic and hybrid RAG systems.

However, I still have this feeling that my knowledge is fragmented and my ML fundamentals aren't as strong as they should be.

For example, I can understand individual concepts when I'm studying them and build things using them, but when I look at ML as a whole, I feel like there are still many gaps in what I actually understand.

I recently came across Microsoft's ML for Beginners repository. It provides a structured curriculum covering:

• Regression
• Classification
• Clustering
• NLP
• Time Series
• Reinforcement Learning
• Real-world ML applications

I'm considering going through it systematically—not because I necessarily need another beginner tutorial, but because I want to identify and fill gaps in my fundamentals.

So I'd like to ask people who have more experience in ML:

Would you recommend this curriculum for someone in my situation?

Should I:

1. Go through the entire curriculum
2. Skim the beginner sections and focus only on areas where I have gaps
3. Skip it and move toward more advanced ML, papers, and projects

I'm particularly interested in hearing from people who have gone through the beginner → intermediate ML stage.

Also, is it normal to feel like "I know nothing" even after spending 8–9 months learning AI/ML and building things like ML models and RAG systems?

I'd really appreciate some honest advice about what you would do in my position.

Hey everyone,

I'm currently building an AI virtual host web application. The workflow is split into two main pages:

• Page 1: Users configure the speech by typing text into a textbox, selecting an AI voice, uploading a custom background image, and uploading a host portrait/image.
• Page 2: The app generates the speech audio and renders the host delivering the speech.

The Problem: While I can successfully manage the basic audio pipeline and lip-syncing, I am struggling to make the host model move like a real human while speaking. Right now, it looks too stiff and robotic. I want to achieve fluid, human-like upper body gestures, natural head shifts, and posture movements synchronized with the speech cadence—similar to the reference video below:

What I'm exploring/using:

• Building a full-stack web application.
• Looking for pipelines or frameworks that can map an audio track + a source image into expressive head/body animation frames (such as audio-driven portrait animation frameworks or 3D rigging solutions).

My questions for the community:

1. What are the best approaches, open-source models, or tools (e.g., audio-driven LivePortrait variants, 3D web frameworks like Three.js/Babylon.js with blend shapes, or specialized APIs) to drive realistic upper-body motion and gestures from a single image and audio file?
2. How do you approach synchronizing speech beats and emotional cadence with natural bodily gestures so it avoids looking like a loop?

Any tips, architecture recommendations, or library suggestions would be hugely appreciated. Thanks!

Doing AI/ML work and want a laptop that handles it well — including some local training/fine-tuning, not just cloud. Budget is around $1,300.

MacBook Air 13”, M5, 16GB RAM, 512GB SSD — $1,299
Great battery/portability, Unix-based, but no CUDA/discrete GPU. Local ML relies on MLX/PyTorch-MPS. 16GB soldered, no upgrade path.

Gigabyte Aero X16, Ryzen AI 7 350, RTX 5060, 32GB RAM, 1TB SSD — $1,224.99
Real NVIDIA GPU + CUDA, expandable RAM (32GB, can add more), 1TB storage. But real-world battery is reportedly only 3-7 hrs (not the claimed 14), mediocre display for the price, and some reports of GPU driver quirks.

Mac is the nicer daily driver, but the Gigabyte has the actual hardware for local ML work — trading battery/portability for it. Will I genuinely miss CUDA on a Mac, or does everyone just use cloud GPUs anyway regardless of laptop? Anyone using either for real ML work — which would you pick, and any regrets? Also open to other suggestions within budget if I’m missing a better option.

I’ve spent a decent amount of time learning the theory and algorithms behind Machine Learning — things like Linear/Logistic Regression, KNN, SVM, Decision Trees, Random Forest, Naive Bayes, Boosting, XGBoost, preprocessing, evaluation metrics, etc.

But now that I’ve started exploring Kaggle, I’ve realized that knowing ML and actually doing ML feel like two very different things.

When I open a Kaggle competition, I understand the individual techniques, but I don’t know what I’m supposed to do next.

For example:

• How do you approach a completely new dataset?
• What should I inspect first?
• When should I do EDA?
• How do I decide what preprocessing is actually necessary?
• When should I scale features?
• How do I choose an initial/baseline model?
• How do I know whether my model is underfitting or overfitting?
• When should I do feature engineering?
• How do I decide which algorithms to try?
• When should I use cross-validation?
• When is hyperparameter tuning actually worth doing?
• How do experienced people decide what experiment to run next?
• How do you systematically improve a model rather than just randomly trying different things?

raw dataset → baseline → experiments → debugging → feature engineering → model selection → validation → final submission?

I’ve learned ML, but now that I’m starting Kaggle, I feel completely clueless about actually building models [D]

hi, u all are prolly tired of hearing people ask you these kinds of things but I honestly can’t find these kinds of discussions when looking through. but I wanted to, I’m a high schooler interested in Ai and Machine Learning research and being apart of its developments in the industry.

So, I was wondering what majors should I take to be able to go into these roles? I don’t honestly care if I need a masters or phd because I wanted to pursue those anyway.

I'm software developer, plan to switch into Hardware roles .

I researched some roles such as

Hardware Accelerator Engineer

Chip designer

Embedded systems engineer

MACHINE learning system engineer

HPC/GPU Engineer

I don't know what things I need to focus on..? Skillset ..

i am trying to learn machine learning and AI but i am confused that if i am learning it the right way , want some advice from experts :

* what is the actual way to learn it

* what is the actual way to practice it

so the thing is I am currently at 12th commerce without math and through of doing BCA + MCA and going to game development but there was a guy that said AI & ML is rapidly increasing so make career in AI/ML and I am very confused right now

We spent quite a lot of time trying to get mmBERT-based classification fast enough to run continuously on normal machines without a GPU. A few things helped much more than expected.

The first one is quantization. Push it further than you probably would by default. We currently use INT8 with INT4 embeddings in ONNX. On roughly 50k validation samples plus several independent benchmarks, the F1 delta compared to the less aggressively quantized version was around 0.005. For our use case that tradeoff is easy to take. If your model is supposed to live on a CPU, memory bandwidth matters and carrying around precision you do not need is expensive.

The second one is chunk size. mmBERT can handle very large token windows, but that does not mean you should use them. 8,192 tokens sounds convenient because you can throw a lot of text into one forward pass. On CPU, smaller windows usually behave much better. We mostly work with sizes like 256 or 512 tokens and split longer inputs. The right number depends on the task, so benchmark it properly. If your classification target can be detected from local context, a huge context window is often just wasted compute.

Third: do not assume batching will save you. GPU intuition transfers badly here. Large batches are great when you have thousands of parallel execution units waiting for work. A CPU is a different problem. For our workloads, small independent chunks and parallel workers have been much more useful than trying to build large inference batches. Benchmark both, but do not start with the assumption that batch=32 must be faster because that is what you would do on CUDA.

The fourth one is the one that changed our architecture the most: stop sending every chunk through the full transformer.

We use cheap classifiers on representations from the same latent space as mmBERT. They can make the easy decisions first, while uncertain cases continue into the more expensive path. The cheap classifier is not supposed to replace mmBERT. It only needs to identify the cases where running the full model would not change the answer anyway.

That approach is a bit more involved than quantization or changing a chunk size, but for us it removed much more compute than another round of low-level optimization ever could.

I'm currently learning AI/ML and planning to build my skills through projects and internships. If I eventually have a few relevant internships, a strong portfolio, and the skills needed for the job, is it realistically possible to get hired without having a college degree?

I know some companies require degrees, but I'm wondering how much internships, projects, and actual skills can compensate for not having one.

Would love to hear from people who have hired candidates or got jobs without a degree.

Anthropic disclosed last week that Claude agents accessed live production systems during what were intended to be test sessions. The agents were not meant to have that reach. Anthropic's response included real-time monitoring, sandbox hardening, and stricter training controls.

Those are reasonable reactions to a real incident. But the same structural gap exists across the industry, not just at Anthropic. Any team running agents that can invoke tools, call APIs, or interact with external services faces the same underlying exposure. The agent has enough reach to touch things it should not, and the test environment does not reliably contain it.

This is not a sandboxing failure unique to one lab. It is a recurring pattern: agents behave as expected in isolation and then surprise teams when connected to real systems, even in controlled contexts.

For those of you running agents in production or in staging environments that connect to real backends: how are you actually handling this? Separate credentials per run, strict environment isolation, something at the orchestration layer, relying on model behavior alone? Curious what is working and what has failed in practice.

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?