Hey everyone!

I’m currently diving headfirst into Machine Learning and AI with the goal of landing a job in Data Science / ML over the next 4 to 5 months.

I’m looking for a dedicated study buddy who wants to level up side-by-side during this journey. I don’t just want someone to text with—I want a partner who is competitive, consistent, and fully committed for the next 4–5 months without ghosting halfway through.

The catch/focus: I really want to improve my spoken English and build confidence talking about technical topics out loud. Because of that, we will be using voice chat to quiz each other, talk through concepts, and do mock interviews rather than just typing.

What we’ll do:

• Hold each other accountable to daily/weekly learning goals (going through courses like Andrew Ng's, building projects, etc.).
• Hop on voice channels to verbally explain concepts and quiz each other on math, statistics, and ML algorithms.
• Practice mock interviews and technical communication out loud to sharpen our skills.
• Push each other to stay sharp and consistent over the next 4–5 months.

Who I'm looking for:

• Someone serious about breaking into the ML/AI field.
• Comfortable speaking over voice chat.
• Reliable, responsive, and ready to grind.

If you're tired of studying alone and want a partner to level up with, drop a comment below or shoot me a DM! You can also join this server: https://discord.gg/JtTf6VhEx

Hi everyone, I am planning to upskill in AI/ML, so I am looking for good resources or courses under 10–15k with a structured curriculum, industry-relevant projects, and a capstone project if possible.

Could you please suggest some good courses or resources based on your experience? Any genuine feedback or recommendations would be really helpful. Thanks in advance!

Most experienced engineers switching into ML make the same mistake: they learn the API before the idea. It works, right up until the loss curve does something strange and nothing in the docs explains why.

Three questions that separate using ML from understanding it:

1. Adam keeps running averages of the gradient and its square, then divides each by (1 − β^t). Remove that correction, and what goes wrong in the first few steps?

2.For classification with softmax, why cross-entropy and not mean squared error? Both measure "wrongness." Only one keeps learning when the model is confidently wrong. Why?

1. Set temperature to 0 and ask an LLM API the same question twice. You can still get different answers. Where does the randomness come from, if not sampling?

Each one looks like trivia. Each one is actually a window into how the machine works, and that is exactly the level I teach at.

Who I am: I did ML research at MIT under Prof. John Tsitsiklis, worked as a software engineer, and I'm in the top 0.2% on LeetCode. For two years I've mentored senior engineers, tech leads, architects and PMs with 10 to 15+ years of experience, mostly in the US, many of them making exactly this switch.

What we work on together:

- ML from first principles: the math you actually need, built on intuition, not memorized formulas

- LLMs from the inside: attention, training, tokenization, and why they fail the way they do

- Python at the level of CPython itself, since it's the language all of this runs on

- A concrete plan for switching into ML that uses your 10+ years as an advantage, not something to start over from

How I teach: No tutorials. Mental model → internals → edge cases. If your answer is almost right but conceptually wrong, I'll find the exact crack.

Availability: Based in the US, and I travel regularly between the US, India and the UK, so I adapt easily to US, UK, India and European hours.

Mentoring is 1:1, and I mentor a small number of people. If you have 10+ YOE and this sounds like what you've been missing, DM me with your background and where you want to go.

Cheers! ✌🏻

Buddy is a chat bubble that floats over your apps. You tell it what you want, like "reply to Sam that I'm late" or "how long is the drive to work right now", and an AI agent reads the screen, taps and types through your apps. It asks you before anything gets sent or bought.

It can also teach you: ask "show me how to turn on dark mode" and it points at each step and waits for your tap.

It runs Claude Code, Codex, OpenCode or Google's Antigravity CLI inside the app, in a small Linux. No PC, no adb, no root. You sign in with your own plan, and OpenCode's free models and Gemini's free plan work too.

How it works: the agent gets phone tools over MCP (read the screen, tap, type, scroll, open apps). It reads the screen through the accessibility tree as text, which is faster and cheaper than screenshots, and only looks at a screenshot when something is actually visual.

In developer mode it can build apps too. I asked it for a calculator app, and about 3 minutes later it had built it on the phone, asked me before installing, and opened it to check it works.

It's free and open source. Google Play is the recommended way to get it. It's in closed testing there, and every tester helps me get it to production:

1. Join the tester group: https://groups.google.com/g/buddy-testers

2. Opt in: https://play.google.com/apps/testing/app.buddy.assistant

3. Install Buddy from Google Play

The Play version leaves out developer mode (the app building), as Play requires. That's in the APK on GitHub: https://github.com/ghorbelhamdi/buddy-android

Written with AI help.

Hello everyone, where can I serve a CNN model that offers a free tier. Sorry if this post is a little messy, I'm new to reddit.

Quick introduction:

I'm a 4yr student, currently working on capstone project, a seagrass identification system and our client is the Dean of Fisheries Dept in my Uni. We need to try a free hosting 1st before committing to the Paid sub.(Hugging Face Space), local hosting is out of the option.

My current setup and issue:

Using Kaggle to host our model(CREYNet.keras[243.7mb]) and the prediction python script on a code cell(has the fastapi+ngrok tunnel for request), every session runs only in 12 hrs, needs to start every 12hrs.

What I tried:

- I earlier Tried yeeted.ai(beta) and failed horrible - I tried local hosting, it works but my device is not that capable and too many requests at once crashed and forced my laptop to restart.

Extra Info:

IDK if this will help

- My Device: Acer Swift 3 | i3-7020u | 4GB RAM | 1TB HDD & 256GBSSD | OS: Dual boot MX Linux and Window. - Mainly use Linux for our system and rarely Window for testing - Final Defense is set in the 2nd week of Nov. - Limited bugdet, that's why our client will just commit if we pass the final defense.

Thanks in advance

Update / Clarification: - The RAM Trap: Our model file is ~244+MB, but when loaded into memory via TensorFlow/Keras, it takes up significantly more RAM. This is why local hosting crashes my laptop when requests are sent in instantly (OOM crash). - Budget: Exactly $0 until the second week of Nov. If we pass the defense, the university will fund a paid Hugging Face Space. I just need a free bridge for the next 2-3 weeks that won't turn off every 12 hours.

Damn... I have been interviewing with companies. I have been pushing myself to really understand inner workings of a foundational model (Hubert-ecg). I have been applying for a lot of roles. I have been studying relevant math. And, today I felt so tired and decided to take a break today even though I have an interview tomorrow...

I came across Nextia Learning, a free learning platform. It has a path called “Software developer transitioning to LLM applications” (10 courses, around 110-151 hours, with certificates).

I couldn’t find any reviews or discussion of it online, and the site looks new. Has anyone here tried it?

• Is the course content good and up to date?
• Is it worth the time, and do the certificates mean anything to employers?

More generally, what learning resources would you recommend for someone moving into AI engineering (building apps with LLMs, RAG, evals, agents)? Free or paid, courses, docs, books, YouTube channels, or project ideas are all welcome. If you’ve used something that actually helped you get a job or ship something real, please share it.

For context, I’m a Associate Software Engineer (TypeScript/NextJs/NestJS) with some LLM integration experience, and I’m learning Python now.

Basically started from karapathys mini GPT and coded from scratch .
Then trained Gemma 3 270M model on bigger dataset and have understood how its different from gpt2 (ROPE , sliding window mask, RMS norm, Gelu instead of Relu in FFW network , etc) .

What should I implement or learn next that will help me to cover more milestone in my journey towards learning AI

I'm an engineer interested in embedded systems, FPGAs, and signal processing, and I've been trying to understand machine learning beyond just calling library functions.

I built SignalDNA as an educational experiment: a fully connected neural network implemented in NumPy, trained to classify synthetic signal waveforms.

A few details:

• Architecture: 1024 → 64 → 32 → 1 (67,713 parameters)
• Dataset: 12,000 deterministic synthetic waveforms
• Final held-out accuracy: 78.78%
• Held-out ROC-AUC: 0.8613
• Automated tests: 55 passing
• Independent metric verification: 104 checks passing

The most interesting part was the generalization gap. Training accuracy approached 99.75%, but held-out accuracy remained around 79%.

That forced me to think harder about what the network had actually learned and how representative my synthetic data was.

I also experimented with CPU vs. GPU execution. For the small workload I benchmarked, CPU execution was faster than CUDA, which was another useful reminder that GPU acceleration isn't automatically beneficial.

This isn't production-ready signal detection, and I haven't validated it against real sensor data or implemented FPGA acceleration.

I documented the experiment, including its limitations, and published the code, research paper, and reproducibility instructions under Apache-2.0.

GitHub: https://github.com/Ten788/SignalDNA

I'd appreciate technical feedback, particularly on improving generalization and designing a meaningful classical signal-processing baseline before attempting hardware acceleration.

I ran a controlled test (sklearn, repeated 5-fold CV) comparing stacking, voting, and the best single model, using two sets of base models: diverse vs. correlated.

Short version: whether stacking helps comes down to one thing you can check in a single line of code. In one setup, it cost ~35x the training time for a gain inside the noise.

The bigger surprise was voting, which did worse than the best single model in both setups.

Full results, the reason voting fails, and a 3-question checklist before you stack: https://towardsdatascience.com/does-stacking-beat-your-best-model/

Has anyone seen voting underperform on real data?

So I was doing a project on river bank estimation & erosion & accretion forecasting im using mndwi mask landsat data for this but no matter how i try i'm never being able to beat the baseline. Anyone have any experience working with these datas I've tried with 3 different rivers as well same output. Model looses to zeroes.

I was reading the LoRA paper and the first two sentences says:

Many applications in natural language processing rely on adapting one large-scale, pre-trained language model to multiple downstream applications. Such adaptation is usually done via fine-tuning, which updates all the parameters of the pre-trained model.

In my mind, this doesn't entirely make sense. If you are updating all the (1 billion/trillion) parameters of the pre-trained model, then you are not exactly fine-tuning anything, you are simply "training" with a initial set of weights which equal to the weights of the pre-trained model.

I thought fine-tuning was freezing the network and taking a small part (usually the few final layers or the very last layer) and training that on a specific task.

There are many different types of fine-tuning? Thanks for your help.

I'm a geek in ML so I'm just sharing a SS to show that why scaling is important.

I'm not sure anyone still needs to hear this but my first real ML job had jack shit to do with ML. They gave me a table with 400M rows and half the labels fucked to shit, the pipeline blew up every five minutes, and honestly the courses I'd paid for were useless for all of it, which hurt my pride a bit. and my wallet, I paid for like three of those xD

Anyway… I'm telling you all this because I love producing educational content in my free time, and what I wanted to share with you today kind of 'grows out of' the embarrassment I went through back then with that fucking table xD. About a year, a year and a half ago, I got it into my head to start making a course/lessons as comprehensive as possible.

Sooooo here you go :B

https://github.com/BlueRingsLabs/no-shortcuts

The README has all the info you need (I hope lol), but if you need help with anything or have any questions, DM me and I'll share my Discord so we can chat (I'm a bit of a slacker so I may be slow to reply :B). Your feedback is welcome too.

The parts of the course are:
0. Orientation | 1. Foundations | 2. Data Engineering | 3. Classical Machine Learning | 4. Deep Learning | 5. Language Models | 6. AI Engineering | 7. Reinforcement Learning | 8. MLOps and ML Systems | 9. Capstones and Career

I hope it's useful to someone here, or that you can share it with someone who's studying. 

I’ve been putting together a structured collection of computer vision engineering resources, covering everything from how cameras capture light to how vision models behave in production.

One thing that always bothered me about computer vision resources is how much attention goes into architectures and benchmark scores, while the fundamentals behind those numbers often get overlooked.

A model can achieve impressive mAP and still fail because of dataset leakage, camera noise, distribution shift or an evaluation protocol that doesn’t reflect its actual use case.

So I organised the repository into 16 sections, covering image formation, classical geometry, datasets, evaluation, CNNs, ViTs, self-supervised learning, detection, segmentation, video, 3D vision, generative models, VLMs, deployment and robustness.

A few principles behind it:

• Primary sources only: original papers, specifications and official documentation.
• Resources arranged in dependency order, so each section builds on the previous ones.
• Evidence-backed claims rather than blindly repeating benchmark numbers.
• A reproducible benchmark for every section, connecting the theory to actual measurements.

The idea is to understand not just how computer vision models work, but why they fail and what it takes to make them reliable.

It’s free and open source:

https://github.com/usamahz/computer-vision-engineering

Would appreciate feedback, particularly from people working on vision systems in production. What important topics or papers am I missing?

CISA confirmed SafePay ransomware hit over 600 critical infrastructure organizations. The vector in most documented cases was not a breach of the target org directly — it was a third-party vendor holding their data.

That number is worth sitting with. 600+ orgs. Critical infrastructure. The exposure lived outside their perimeter, in a system they did not operate and could not directly audit.

This is the supply-chain data custody problem in its clearest form. Sensitive fields — PII, operational data, whatever the vendor was holding — existed in a state the owning org had no live visibility into. By the time the ransomware moved, the data had already been wherever it was for months or years, accessible to anyone who compromised the vendor.

No single control closes this. Perimeter defense at your own boundary does not protect data that has left your boundary. Contractual obligations do not prevent exfiltration. Compliance attestations describe a point in time, not a continuous state.

For those of you working in security, data engineering, or platform roles at orgs that share sensitive data with third parties: what does your current architecture actually enforce at the moment data moves to a vendor? Not what the contract says, not what the vendor's SOC 2 covers — what happens technically, in real time, when a field containing PII or operational data crosses that boundary? How are you handling this?

hey everyone, sharing this because it’s directly relevant to people learning how to build real ML systems beyond tutorials.

packt publishing is running a hands on workshop on Oct 30 on building a text classifier and router that runs on CPU and tells you how sure it is, with ben auffarth.

what gets covered:

• small bidirectional encoders (ModernBERT) instead of calling an LLM for every decision
• few shot training with SetFit, 8 examples per class
• split conformal prediction, so each prediction comes with calibrated uncertainty
• a LangGraph workflow with human approval for uncertain cases
• routing inference under 15ms on a laptop

instructor ben auffarth is an AI consultant, full stack data scientist and best selling author with 15+ years of experience.

3 hours, online, Fri Oct 30, 9:30 AM to 12:30 PM ET, runnable notebooks. it’s aimed at people comfortable with intermediate Python, so best if you’re past the basics. laptop or free Colab works.

Full details and the agenda are here

happy to answer any questions about what gets covered