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! ✌🏻

I have 2 years of work experience, currently working as GenAI engineer but hoping to get Computer Vision roles.

My job applications are getting constant rejections. Don't know if the problem is in the resume or the experience.

What changes / projects would you suggest to get noticed / shortlisted by companies?

Thanks in advance for suggestions

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!

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.

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.

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?

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. 

Knowledge distillation started as a simple idea in a 2015 paper by Hinton, Vinyals and Dean: train a small model to imitate a big one. Now Anthropic and OpenAI say it’s being used at massive scale to copy their models through millions of API queries, and it’s surprisingly hard to block because the queries look like normal usage.

I made a short animated explainer covering how distillation works, what the labs reported, and what it means for open-source models. Sources are in the description. Curious what people here think: should this count as “stealing,” or is it just learning from outputs?

Hey everyone,

I put together a complete Python ML reference repo that covers everything I kept having to Google while learning:

📌 What's inside:

• NumPy — arrays, broadcasting, linear algebra

• Pandas — DataFrames, groupby, merge, datetime

• Scikit-learn — every model, pipelines, cross-validation, hyperparameter tuning

• Matplotlib & Seaborn — every plot type with code

• Data Preprocessing — scaling, encoding, missing values, class imbalance

• Feature Engineering — datetime features, target encoding, PCA, SHAP

• Model Evaluation — ROC, PR curves, confusion matrix, SHAP explainability

• Deep Learning (Keras) — CNNs, RNNs, transfer learning, callbacks

• Statistics for ML — hypothesis tests, A/B testing, distributions

Everything is plain Markdown — no setup, no notebooks to run. Just open it on GitHub and Ctrl+F what you need.

🔗 https://github.com/Kavi274/python-ml-cheatsheets

Hope it saves someone a Google search. Happy to add more topics — PRs welcome!

Hi Everyone ,
I’M really very bad at math. Can anyone share the guide for Math for ML which is easy to understand for a layman

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

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 made a short visual explainer on supervised learning, using the idea of a teacher checking homework.

It covers:

• What a labelled dataset is
• Classification vs regression
• How training and testing work
• Why good labels are essential
• Real examples such as spam detection and price prediction

Watch here: https://youtu.be/Yp8z16DIE1A

If you find it helpful, please like, share, and subscribe to my channel for more beginner-friendly AI and machine learning videos.

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’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?

Hey everyone

I've been learning about Transformers and LLMs, so I built KERN,

a 30M parameter GPT-style language model in PyTorch,trained from scratch on TinyStories.

GitHub: https://github.com/OmarDawoud4/KERN

This is still a work in progress! I'd like to keep improving it by implementing techniques like LoRA, KV caching, and knowledge distillation from GPT-2.

I'd appreciate any feedback or suggestions .