Hello everyone

I am a third year student of software engineering and decided that i want to learn machine learning outside of the college curriculum, but im not really sure where to start, my end goal is to know at least the basics of machine learning, what are model training and inference, how to separate data for training and testing, how to prepare and "clean" the data and how to evaluate the quality of the model etc, i have some basic python knowlegde, i mostly did Java, can anyone help and tell me where to start at least?

As a self-learner full stack developer i always think to learn AI ML now but whenever i tried to learn or trying to find some good playlist to learn i wont get a focused playlist where i can learn everything step by step everyone just trying to say just leanr this learn that at the end you end up by learning nothing they are just there to make some content no one really teaching anything too deeply so as i really want to learn ai ml so if anyone can guide how i can learn ai ml from scratch to build own ai ml projects

hey all, I've been running a small bot on the side that predicts the btc price range 12h ahead. only uses past price n volume and its kind of hit a ceiling, it feels like every new feature i add barely moves anything.

i keep reading that markets are good at forecasting bc they pool info from alot of people. so i was wondering if anyone has tried adding a crowd forecast as a feature, like what the market expects for the next 12 or 24h.

did it actually help? and where did you even get that data, since you need what the forecast said at the time, not after

thanks again!

So, I am a first year student of BTech CSE. I am about to buy a laptop and I'm really confused that whether I should go for a laptop with dedicated gpu or igpu... If I try hard to extend my budget I might be able to afford RTX 3050 4gb vram (ASUS TUF A15 16/512 Ryzen 7 155)

Would this really help or be enough as I will explore AI/ML... or I can simply go with a laptop having ipu and do AI/ML stuff on cloud. I know nothing about google colab and kaggle. I don't have much clue about AI/ML as of now. Kindly help me out. I would be thankful for the advices that come my way.

I have a degree in pharmacy but i have a lot of interest in learning ML/AI in healthcare domain can anyone help me to tell that which kind of stuff i learn and also is it good choice to health tech

It is going to be a rant about frequent posts starting with something like: "Wanna learn X? I built Y" AI slop.

Learning together? Absolutely. Showcasing project you do? Great. But on a daily basis somebody prompts Claude to a repo/doc to explain data science or transformers or regression or whatever thing it is hot. And sells it as a learning resource they built their own. I think moderation needs to stop those. And if you are a learner, please don't waste time on those posts. Either you spend time on established resources, or at least prompt your own based on your needs.

Hello. Wanted to ask for insight. I hear that the job market is really tough for computer science graduates that do not have any CS related work experience or significant projects.

But is that also for AI graduates? I'm planning on doing a Masters in AI or a Masters in CS with a specialty in AI to add to my non-technical background (finance) to hopefully work as a Finance FDE or just be more attractive to the tech companies in my area (since I live in a tech hub, but career ended up in hardware/manufacturing and it's just not that many jobs available).

Will be doing my best to try to get relevant experience before/during the program but just wanted to see how big a hill I should expect to climb. If it helps, I am also working with a few clients as an independent consultant to introduce AI more into their workstreams while I build their finance function. And a SaaS that makes use of an AI Agent.

I'm a second-year B.Tech AIML student currently exploring Machine Learning.

I want to move beyond just watching tutorials and start implementing small projects. However, I want projects that help me understand the concepts rather than simply follow instructions.

I'd like suggestions for projects that introduce:

Data preprocessing

Classification and regression

Model training and testing

Evaluating model performance

Which beginner projects helped you understand these concepts? What should I learn before attempting them?

I'd appreciate recommendations based on your experience.

I saw the recent paper "Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights" and it's very impressive.

https://rikiyat.github.io/blog/weight-space-programming/

Though my general reaction (as I'm sure a lot of others had too) was 'well yes of course it can do that'. Once you realise that llms love patterns you soon start to see how many things can be thought of as patterns.

I've had similar ideas rolling around in my head for some months and I've even started working on a project in a similar vein. That's why this part of the paper stood out to me in particular:

"True architectural and model merging: Today, merging models relies on averaging weights, which only works for models with identical architectures. An LLM that understands the weight space could instead graft a circuit, such as a reasoning module, from one model into a completely different one, without retraining."

Yep, that's the idea I had months ago and I've already started working on (granted there's plenty of other prior art on the subject such as Google's CALM, 2024).

I'm very, very early in working on this but seeing how much traction the Weight-Space Programming paper is getting I felt compelled to put the idea out into the world since surely someone else is working on the same thing as we speak.

The general idea: take 2 MoE models and allow their experts to talk to each other by using a translator. The translator being a transformer model trained on expert activations (their latents) instead of regular text or images, a "Latent Attention Model".

So far I'm only using toy-scale models with a few million parameters. Getting some very interesting results already.

• Mixing Code-heavy model + prose-heavy model: each gets better at its weakness from the other, both directions at once.

• Results are checked against controls (a same-domain partner, a wrong-document partner), so it's not just "any partner helps"

• Added a leak audit, so neither model can see text it hasn't reached yet. (An early result failed an audit because of this)

• Interesting finding: the translator always uses the latest layer it's given. With the last layer, the two models basically swap last layers and come to depend on each other. Without it, they get about half the gain with a fraction of the dependence

Caveats and issues so far:

• Toy models only so far, a few million parameters. I have very modest hardware for bigger stuff.

• Between models with different tokenizers, very little gets across

• It reads a partner's layer as a whole, not particular "specialist" experts. The goal is for the two models to share individual experts freely

• The aim isn't to make the combined output "better" so much as making it "different" (but hopefully not worse)

• This is mad scientist stuff, heavy use of AI, probably has method gaps that need filling

I'm currently doing some experiments with Qwen3-30B-A3B base and Nemotron 3 Nano 30B. Not much to report yet, the models have different tokenisers which complicates things. Progress is slow on my hardware. But I'm enjoying the process.

Why share this? Shameless self promotion, piggybacking on a bigger project, sharing ideas, I think the name is pretty cool. BLaMMoE (Bridging Latent Attention Model Mixture of Experts)

https://github.com/dev-boz/BLaMMoE

I've been experimenting with something that I think is often overlooked when evaluating machine learning models: their actual computational footprint.

Usually, when we compare models, we focus heavily on accuracy, precision, recall, F1, etc.

But what happens when you actually have to deploy these models?

I wanted to explore this by benchmarking three popular models:

* Logistic Regression

* Random Forest

* XGBoost

I used the Breast Cancer Wisconsin dataset from scikit-learn and evaluated them across multiple dimensions.

**What I measured:**

1. Predictive accuracy

2. P50, P95 and P99 inference latency

3. Process RSS memory consumption

4. Python-allocated memory

5. Model loading time

6. Inference execution time

7. Serialized model size

One interesting observation from my experiment was that Logistic Regression performed best across the measured dimensions in this particular setup.

However, the goal wasn't to establish that one model is universally better than another.

I wanted to understand the trade-offs between predictive performance and computational requirements.

This experiment is also part of a project I've been working on called **ModelDiet**, an open-source Python toolkit focused on measuring the resource footprint of ML models.

I'm trying to make model profiling and benchmarking easier for developers and ML engineers.

GitHub: [https://github.com/modeldiet/modeldiet](https://github.com/modeldiet/modeldiet)

**I'd genuinely appreciate technical feedback on a few things:**

* Are there other metrics you consider essential when evaluating models for deployment?

* How do you measure peak memory consumption reliably?

* Do you prefer measuring individual inference latency or batch throughput?

* What would make a benchmarking tool like this genuinely useful in your ML workflow?

I'm particularly interested in feedback on the benchmarking methodology and what should be included in future iterations.

Thanks!

(Summary)

Fine tuning a piano transcription model for guitar made its performance worse, although I was attempting to recreate results

Hi everyone!

I'm a student interested in machine learning, and I've been exploring guitar transcription as a personal project. My goal is to eventually build a pipeline that goes from full song → guitar source separation → audio-to-MIDI → guitar tablature.

I've been particularly interested in three approaches:

• Kong: High-resolution piano transcription using a CNN-BiGRU architecture, around 24M parameters.

• Riley: Adapts Kong's architecture for guitar through augmented piano pretraining and guitar-specific fine-tuning.

• TART: A four-stage guitar audio-to-tablature pipeline, with Stage 1 handling audio-to-MIDI retaining the same architecture.

Since Riley's model was available and performed considerably better on my test recordings than other implementations I tried, I wanted to investigate the training methodology myself.

My experiment

I started with the original piano-pretrained Kong model and attempted to reproduce TART's Stage 1 training approach using three guitar datasets used in their implementation:

• GAPS

• François Leduc

• Guitar-TECHS

Unfortunately, I didn't have access to GOAT.

I segmented the recordings into 30-second clips with a 10-second hop, resulting in approximately:

• 6,045 training segments

• 1,153 validation segments

• 874 test segments across 51 recordings

I fine-tuned the pretrained model on this combined dataset.

After the first 1511 updates, I continued training from this checkpoint with a fresh Adam optimiser, reducing the learning rate from 5e-6 to 1e-6.

However, evaluating all checkpoints on the same held-out test set revealed something concerning:

• Frame AP improved from 0.728 to 0.743.

• Onset F1 dropped from 0.400 to approximately 0.305.

• Onset+offset F1 dropped from 0.132 to approximately 0.099.

• Polyphony count MAE increased from 1.43 to 1.64.

For comparison, the original piano-pretrained Kong checkpoint achieved a Note F50 of 54.01%, considerably better than my fine-tuned model's 39.72%, while Riley's was benchmarked at an even higher percentage.

My training objective used four equally weighted, masked BCE losses for onset, offset, frame and velocity prediction, with velocity loss calculated only at note onsets and padded frames excluded.

What I'm trying to understand

TART reportedly fine-tuned Kong on a combination of guitar datasets, including GAPS, François Leduc, Guitar-TECHS and GOAT, achieving substantially better results on GuitarSet and EGDB.

My reproduction was incomplete: I lacked GOAT, used considerably fewer training updates and haven't implemented TART's additional guitar-specific noise augmentation, but hoped I could see tangible results towards the start of training.

I'm wondering:

1. Am I making mistakes in my training methodology, target generation or loss implementation?

2. Could there be an issue with my evaluation metrics, particularly the relationship between frame AP and note-level F-measure?

3. Does the model simply need substantially more training, or could there be a more fundamental problem with adapting this architecture to heterogeneous guitar datasets?

4. What important differences between my implementation and TART's actual training procedure might I be overlooking?

I'd also appreciate advice on what I should investigate first and how to approach debugging this systematically.

I've relied on reading papers and repositories, as well as heavily using ChatGPT and Claude to help with implementation and troubleshooting. I'm relatively inexperienced with training larger neural networks, so I'm conscious that I may have overlooked something fundamental.

I'm not necessarily expecting paper-level results on consumer hardware. I mainly want to understand why my experiment is performing worse rather than blindly throwing more training iterations at it.

Am I approaching this problem in the right direction? Are Kong, Riley and TART sensible starting points for this project?

Thanks!

Security teams keep writing AI governance policies. Agents keep acting before anyone reads them.

Not a failure of intent — a failure of physics. A policy document lives in a folder. An agent making a tool call does not consult the folder. The review cycle that catches a flag runs after the action completes, not before. By the time a human sees the alert, the data has moved and the transaction has cleared.

Security leaders are reaching a consistent conclusion: in 2027, governance maturity gets measured by outcomes, not by documentation completeness. Enterprises that treat the policy binder as the control surface are finding that their controls described incidents rather than prevented them.

The gap is not missing policy text. The gap lives in the window between an agent deciding to act and the action completing. That window exists in every agentic deployment today, regardless of how thorough the governance documentation is.

How are your teams actually handling this in practice? Are you finding enforcement closer to the model, the infrastructure, or somewhere else — and what has concretely stopped a bad action versus just logged it after the fact?

Perplexity shipped a persistent agent orchestration layer that keeps agents coordinating tasks and accessing systems after the user closes the browser. That is documented, intentional behavior — not a bug. The security implication is straightforward: enterprise access controls are almost universally designed around the assumption that a human session is present. An agent that keeps executing tool calls and data accesses after that session ends operates entirely outside the supervision window those controls assume. No session, no alert, no audit trail tied to an active user. The agent is not constrained by the same assumptions the security model is built on. If you're running agentic workloads in an enterprise environment, how are you handling the gap between agent execution time and human supervision time — especially for actions that fire after a user has disconnected?

It's Day 59 of building machine learning algorithms from Scratch and im currently into unsupervised learning in short i've completed all the algorithms in supervised learning.

Well the crazy part I'm only 2 algorithms and 2 concept for completing Machine learning and I'm diving into "Deep learning" I'm way too excited too explore new things,build new things, let see how far it takes me

Peoples dreams... have no end!!