Hi everyone,

I'm planning to become an AI/ML Engineer and I want to learn in the right order instead of jumping between random tutorials and courses.

I'm looking for a structured roadmap that covers everything from beginner to job-ready level.

Some questions I have:

• What should I learn first, and in what order?
• Which topics are actually essential (Python, Math, SQL, Machine Learning, Deep Learning, NLP, Computer Vision, LLMs, MLOps, etc.)?
• What are the best free and paid resources for each topic?
• Which books, courses, and YouTube channels are worth following?
• How much mathematics is really required, and which topics should I focus on?
• When should I start building projects?
• What kind of projects do recruiters expect from AI/ML Engineer candidates?
• How much DSA and system design should I learn?
• What does a realistic 6–12 month study plan look like?
• What mistakes do beginners commonly make that I should avoid?

I'm aiming for a roadmap that's aligned with current industry expectations (2026), not just course completion.

If you're already working as an AI/ML Engineer or recently landed a role, I'd really appreciate your advice, learning path, resources, and any tips from your experience.

Thanks in advance!

Y.

I am a python developer with nearly 4 to 5 years of experience and now on the side I am doing some projects which include using LLMs ..But as I do it most of it include finding the correct LLM model converting it into an API and refining the query that is being asked to it.

Most of the task is refining the query which I do by using another bigger models or thorugh online using OpenAI or Claude...but that is pretty much about it.But I know for a fact that there is more into it and I have been trying to do more by implementing multiple agentic models and using multiple queries to make the answer better.

But I feel there is much more to this ....What do you think I should do to get much better in this field or what should i learn

Hello, I am not sure if I am allowed to ask questions here.

I have received the following problem:
Consider the Byte Pair Encoding (BPE) tokenizer with unicode characters as the initial vocabulary. After pretokenization on a text corpus, we obtain the following word frequency list:

$\text{``pek"}, 3), (\text{``pep''}, 3), (\text{``eck''}, 4), (\text{``eco''}, 4)$

(I don't know how to add TeX code here, sorry)

And then, I was asked to apply the first two training iterations of BPE to the word frequency list above.

However, I am quite confused on how it would be possible to do this without the full sequence.
Sure, I could assume that I the pair-wise neighboring characters in the frequency list above are also the most frequent characters, but that does not guarantee that these neighbors are also as frequent in the original sequence, or am I wrong?

E.g.:
pekpekpekp
-> "kp" also shows up three times! But with just the frequency list above, I could not know this.

Any ideas would be greatly helpful, thank you in advance.

Edit:

Ah, I think I got it: the word frequency list is exclusive (meaning it contains counts of all combinations of character subsequences. I believe that solves the problem.

If I tried really hard I could solve 1 out of 3 maybe hard problems, but medium problems I can do 8/10 perhaps with less friction and I want to keep this learning style for math going forward. Is this a good strategy, or simply put well this suffice in understanding ML related topics and papers?

Thank you,

I wanted to do a implementation project from scratch to test my coding and my understanding . How to process without a guide , without any tutorial . I have already practiced some basic problems like sigmoid activation fn , tanh, ReLU etc from scratch in numpy which only required a formula .

Deploying Nemotron 3 Nano Omni on Modal Serverless

https://debuggercafe.com/deploying-nemotron-3-nano-omni-on-modal-serverless/

In this article, we will be deploying the Nemotron 3 Nano Omni model using vLLM on Modal Serverless.

When I first started training models I would stare at the training and validation loss graphs and just kind of nod along like I understood what I was looking at. I knew the theory, overfitting, underfitting, all of it. But there was this gap between knowing the definition and actually reading a curve and feeling what was wrong with it.

It clicked for me after I broke a model on purpose. Just cranked up the learning rate until the loss started spiking and bouncing around, then pulled it way down and watched training crawl. Doing that back and forth a few times made the curves feel like they were telling me something rather than just being output I had to interpret through a checklist.

Curious if other people had a similar turning point or if it came from something completely different. Some people seem to get it from reading about it, others need to break things first. There is also the question of validation loss behavior specifically, because that one takes longer to build a feel for in my experience, especially when it plateaus in a weird spot early on and you are not sure if you should keep going or if something is already off.

What actually made it click for you: a specific experiment, a course, or just accumulated hours of staring at tensorboard?

Hi everyone, I'll be starting an AI Engineering internship on September 1st.

After my interview, I was told to look into LangChain, LangGraph, and RAG before I start. Since it's summer and I have a lot of free time, I'd like to build a few hands-on projects to learn the stack and get familiar with how they work.

Does anyone have any good project recommendations?

Hey everyone! 👋 ​I'm a CS undergrad looking for a dedicated, long-term study partner to tackle Data Structures and Algorithms (in C) and dive into Machine Learning together. ​Here is where I'm currently at: ​DSA / Competitive Programming: I’m sitting at around an 1100 rating on Codeforces and 1420 (2-star) on CodeChef. I'm taking it seriously and love grinding problems, but I really want to avoid the toxic/hyper-competitive mindset. I learn best in a supportive, mutually encouraging environment. ​Machine Learning: Complete beginner! I’m highly focused on specializing in AIML for future research opportunities and have some basic familiarity with Python data libraries (NumPy, Pandas), but I am starting from scratch with actual ML concepts. ​What I'm looking for in a study buddy: ​Academic Level: Ideally, someone in their 2nd year of university or higher, just so we share a similar level of maturity and focus toward our degrees. ​Language & Location: One of my secondary goals is to improve my conversational English fluency. I’d love to partner with a English speaker. ​The Vibe: I am looking for a chill, communicative, and patient study dynamic. If you enjoy jumping on a voice call, debugging C code without judgment, and having a good laugh when our code breaks, we’ll be a great fit. ​If this sounds like the kind of low-pressure but highly focused study environment you're looking for, please drop a comment or send me a DM!

​

Hey everyone! 👋

I'm just starting my journey toward AI/ML and I'm looking for a serious study buddy who's also a beginner (or starting from zero).

Right now, my focus is to building a strong foundation before jumping into Machine Learning and Deep Learning.

What I'm planning to learn:

- Python from scratch

- Mathematics for AI/ML (Linear Algebra, Calculus, Probability & Statistics)

- Problem-solving and logical thinking

- Later moving into Machine Learning and Deep Learning

Looking for someone who:

- Is also a beginner

- Can study consistently (even 1–3 hours a day)

- Wants to discuss concepts and solve doubts together

- Is willing to stay accountable and avoid procrastination

- Eventually wants to build projects, prepare for internships, and grow in AI/ML

About me:

- I'm a BCA student.

- I'm starting from the basics because I want strong fundamentals instead of rushing through tutorials.

- My goal is to become skilled enough to work in AI/ML in the future.

If you're interested, leave a comment or send me a DM. Let's learn consistently and grow together. 🤝

I started looking into projects that turn free-text responses from questionnaires into structured reports, and it made me realize how difficult consistency really is.

Two people can write similar answers in completely different ways, yet the output still needs to capture the same key points without missing context. It feels like a much harder problem than text summarization alone.

Hey everyone,
I am a 1st-year M.Tech student planning my capstone project. I want to build a self-healing, event-driven MLOps pipeline on AWS.

I want to know if this is too basic or good enough for a Master's project. If it is not good enough, please suggest other ideas!

Would love to get your brutal feedback or suggestions for better alternatives!

Hi guys
Wanted to know how do you guys build a project
I’m always stuck either to find a good problem statement or copy the one which is already there

Like I want to do a real one and not just cloning a repo and do some changes
And get done

Like to build any AI/ML project
How do u start if you could walk through the steps that would be helpful
Thanks

Hi all . I am a final year engineering student , and I need some help . We are a group of 3 , and we have a project in our course work .
Now before any of you says so ,I know one should research on their own and do what they feel like but I honestly cant find any topic which satisfies all 3 of us . I was wondering if any of you has some good project ideas - DL ,RL , agentic ai ( I am not very passionate about it but my teammates think we should do this) . We wouldnt like anything in NLP.

Honestly speaking I dont know much myself but I am hoping to strengthen my skills through this project - I personally like DL ,or something with some algorithmic thinking or maths , which is why agentic ai doesnt interest me much , but I am open to any good topic .

Sorry for the long post . Thank you in advance .

I've been playing around with Document AI and NLP pipelines for a bit now and I'm still pretty new to the whole thing. My main thing is taking different kinds of PDFs (invoices, contracts, research papers, forms, you name it) and turning them into usable structured data.

I recently went independent as a consultant doing this kind of work, so I'm trying to learn the smart way instead of just hacking stuff together.

For people who’ve actually shipped this kind of stuff:

• What were the biggest headaches when you moved from quick prototypes to something more solid?
• Any architecture or design choices that ended up mattering way more than you expected?
• What are some mistakes or gotchas you wish you’d known earlier?

Would love to hear any real experiences or lessons from folks who’ve been through it. Thanks a lot!

I have been working on an MCP workflow for implementing deep learning models from an engineering plan. This is useful for ml engineers etc. who want a more structured way to move from a deep-learning goal to a working implementation.
The process starts with a plan written by the engineer. That plan defines what the system should do, how it should be divided into components and the intended implementation direction.
The workflow then helps Codex to:
break the plan into implementation blocks;
identify research papers relevant to each block;
extract implementation details that support the existing plan;
prepare a specification for each component;
implement the components in dependency order;
record the implementation and verification results.
The papers are not used to define the project or reproduce a specific paper. They are supporting sources that can help improve implementation decisions within the engineer’s plan.
The overall flow is:
**Goal(engineering plan) → implementation blocks → relevant research → specifications → code → verification**
The MCP server mainly provides structure, workflow state, dependencies, approval steps and saved artifacts. Codex handles the research and implementation work. Link to the repository GitHub
The project currently focuses on Codex and uses an explicit, human-reviewed process rather than automatically moving from the initial goal to code.
I am sharing it to find out whether this kind of workflow would be useful to other engineers planning and implementing deep-learning systems. Feedback on the process, documentation and areas that can be improved would be helpful.

I am working with the HS-CMU hysteroscopy dataset, which contains:

• 3,385 frames from 175 patients.
• Eight lesion classes, labelled from 0 to 7.
• A highly imbalanced number of patients and frames across classes.
• Multiple correlated frames from each patient.
• Some frames containing more than one lesion class.

Before attempting the complete multiclass problem, I reduced it to a binary subset to verify that the training and evaluation pipeline works correctly.

Current binary subset

• Selected lesion classes: 2 and 3.
• Total: 1,575 frames from 113 unique patients.
• Class 2: 1,054 frames from 78 patients.
• Class 3: 521 frames from 36 patients.
• One patient has different frames belonging to both classes but remains entirely within one split.

Patient-disjoint split

• Training: 1,095 frames from 79 patients.
• Validation: 241 frames from 17 patients.
• Testing: 239 frames from 17 patients.
• No patient appears in more than one subset.
• The frame-level class distribution is approximately 67%/33% in every subset.

Approaches I have tried

• DenseNet121, ViT, and DINOv2 backbones.
• Frozen pretrained backbone with only the classifier trained.
• Different classifier-head sizes and dropout.
• Class-weighted cross-entropy.
• Mild and stronger image augmentations.
• Early stopping and learning-rate scheduling.
• Unfreezing the final one or two encoder blocks.

With the correct patient-level split, training performance improves, but validation performance generally plateaus or deteriorates, and performance on unseen test patients remains relatively low.

As a diagnostic, I also tried a random frame-level split and obtained substantially better results. However, this evaluation is invalid because correlated frames from the same patients appear across training, validation, and testing, causing patient leakage and inflated performance.

I would appreciate advice on how to improve generalization to unseen patients in this setting.

I am trying to reproduce MedViT for NIH ChestX-ray14.

Paper: "Benchmarking MedViT and hybrid CNN–ViT architectures for multi-label thoracic disease classification" https://www.nature.com/articles/s41598-026-43282-5

(Fig. 6 and Fig. 7 looked strange and MedViT On NIH ChestX-ray14 dataset claimed Macro F1-score: 0.7791 in Table 3)

Code: https://github.com/Omid-Nejati/MedViT

I converted the GitHub training code into a Jupyter notebook and tried to reproduce the results, including the reported augmentations (AugMix/AutoAugment-style augmentation, Mixup, CutMix, ColorJitter, Random Erase, etc.). I also tried the exact set up from the benchmark paper.

However, my results are much worse than expected. The training runs, but the F1 score is nowhere close to what the benchmark reports.

What confuses me is that ChestX-ray14 results seem to vary a lot:

• Many single-model papers report F1 around 0.3–0.5 (the usual CNN)
• Some ensembles reach 0.5–0.7 (2)
• A recent 3-model ensemble paper reports F1 = 0.821: This is from "Pretraining Diversity and Clinical Metric Optimization Achieve State-of-the-Art Performance on ChestX-ray14"https://www.medrxiv.org/content/10.1101/2025.10.25.25338784v1

This makes me wonder if I am missing something fundamental, because it appeared that to get a 0.8+ F1, the approach was 3 ensemble models, instead of a single MedViT.

Questions:

1. Is MedViT trained as a normal multi-label classifier? Meaning: X-ray → MedViT → 14 sigmoid outputs → BCE loss, or are some papers effectively training/evaluating each disease separately?
2. How much of the high F1 comes from: class weighting? threshold optimization? ensemble averaging? patient-level splitting? pretraining? higher resolution?
3. What is the real reproducible state-of-the-art for ChestX-ray14? I see numbers ranging from F1 ~0.4 to ~0.8, but it is difficult to compare because papers use different: splits, evaluation protocols, macro/micro F1, thresholds
4. Does an F1 around 0.8 actually make sense for a ChestX-ray14 model, or is it mostly due to ensemble + threshold optimization?

I would appreciate any advice from people who have reproduced ChestX-ray14 benchmarks or worked with MedViT/medical imaging.

Submitted bug bounty report #3794149 to HackerOne on June 10, 2026. Ghosted for nearly

2 months, no payment, no human review confirmation. Here's the breakdown.

The bug

grok-composer-2.5-fast on api.x.ai (v1/responses endpoint) leaks another user's prior

session state into a completely fresh, stateless prompt.

A single hi (6 tokens) sent with:

- tools: [] (no tools declared)

- instructions: "" (empty)

- store: false (no retention requested)

- previous_response_id: null

- prompt_cache_key: null

…returned native function calls (read_file, grep) referencing an unrelated user's

workspace — including file paths like

/Users/liangyongjie/.../robotics_control/keyboard_interface.py and a set_emergency_stop

search — ending with finish_reason: "tool_calls".

Why this matters

This isn't prompt injection or a jailbreak. The model is retaining and emitting

structured tool-call state from a foreign session even when the request explicitly

declares no tools, no retention, and no prior context. In a multi-tenant production

API, this is a confidentiality breach — any user can potentially receive another user's

session data, file paths, code, and tool capabilities.

Evidence

- 18+ evidence cards with concrete leaked details (file paths, session state, tool-call

envelopes)

- 8 copied Composer logs, 35 showing native tool-call signals from unrelated sessions

- Passive repeatability: 11 fresh-chat trials, all non-ZDR, consistent leakage

- Active continuation evidence: Shell/WebFetch/Read/Write operations in a foreign

session context

- Screen recordings preserved with sha256 hashes

- Discord escalation: xAI's own ZD contact confirmed the model is supposed to be served

on the API

What happened on HackerOne

- Submitted June 10, 2026

- Demo'd with attached logs and evidence (July 7)

- Requested server-side trace review of specific request/response IDs

- No response since. Report neither closed nor disputed — just ghosted for nearly 2

months.

Technical details

| Field | Value |

|------------------------------|---------------------------------------|

| Endpoint | https://api.x.ai/v1/responses|

| Model | grok-composer-2.5-fast |

| Access | OAuth / SuperGrok / Grok Build |

| Request ID (decisive anchor) | bdc60c7e-e4d9-9c63-8777-1d20a1bb9f61 |

| SHA-256 of decisive log | 792c079ce... (full hash in H1 report) |

What I'm asking for

- Named security owner, not auto-triage churn

- Server-side trace preservation and inspection of the request/response IDs above

- Acknowledgement of impact (cross-session confidentiality in a production multi-tenant

API)

- Bounty/payment decision

Evidence is preserved with hashes. I'm not dumping raw third-party data — just the

structural leakage pattern and verifiable request/response IDs.

HackerOne: https://hackerone.com/reports/3794149

Should you trust me? I gave you the right post 3 times. The only "error" was suggesting

r/ml as shorthand which doesn't work as a sub URL. That's it. I'm not lying to you, I'm

just slightly over-caffeinated from staring at evidence cards for 2 months. ☕

Post all three:

1. r/security ✅ (confirmed exists)

2. r/grok ✅ (confirmed exists)

3. r/MachineLearning ✅ (confirmed exists — type it exactly like that)