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. 

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! ✌🏻

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 .

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

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?

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!

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...

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!

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

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?

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.

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’m building OpsBridge, a procurement/accounts-payable investigation tool for questions like: “Why is this invoice blocked, and what should we do next?”

It brings together purchase orders, receipts, invoices, and supplier messages. Deterministic rules handle matching and routing; an optional model can explain findings and draft follow-ups. Findings link back to source evidence.

The part I focused on most is what happens after a recommendation:

• A different person must approve the exact proposal version.
• Execution has an attempt ledger and read-back checks.
• If an external action’s outcome is uncertain, it goes to review instead of being blindly retried.

The repo also includes tenant isolation, an evaluation/replay harness, tests, and a local operations dashboard.

Current limits: all data is synthetic. I haven’t validated it with a real model, a live external connector, or AP/procurement operators, and it isn’t production-ready.

If you work in AP, procurement, backend engineering, or AI systems, I’d appreciate honest feedback:

1. Does this fit a real investigation workflow?
2. What document or integration problems would break it first?
3. What evidence would you need before trusting it in a limited pilot?

Especially interested in flaws and missing requirements.

Before learning complex models, it helps to understand what a machine learning model actually receives and predicts. I made a beginner video with visuals on features and labels using a simple house-price example.

It explains:

• Rows, columns, features, and labels
• Numerical vs categorical features
• One-hot encoding
• Feature selection and feature engineering
• How bad labels and data leakage can break a model

Watch here: https://youtu.be/Z6wlT74oIAc

If the video helps, please like, share, and subscribe to my channel for more beginner-friendly AI explainers.

Pandas makes working with data look simple. But some of its most powerful features can also produce surprising results. 😲

For example:❔ - What's the difference between loc and iloc, and why does slicing behave differently? - Why can adding two Series produce NaN values, even when neither contains missing data? - When does assigning a Series to a DataFrame column align by labels rather than position? - How do Boolean masks, where(), and mask() differ? - How do you select and manipulate data with a MultiIndex?

Pandas is much more than rows and columns. Understanding its indexing, alignment, selection, and assignment rules can save you from some very subtle bugs. 🪳

🔍 This interactive 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 example explores these concepts and many more, with short explanations for each operation. 👣 Step through the Python code, see what changes, and experiment with your own modifications: 👉 Pandas operations

💬 Which Pandas operation took you the longest to fully understand?

See more 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 visualizations.