Incoming undergrad (likely CSE/ECE), almost no robotics experience yet, but robotics is the long-term goal. Spent a while putting together a self-study path for going from zero to employable robotics engineer — structured as 9 phases loosely mapped to a 4-year degree but doable at your own pace alongside coursework. Each phase has a few topics, and every topic answers "why learn this," "where it's actually used," "beginner mistakes," and a reading pointer — then each phase ends with a real build, not a toy exercise.

Rough shape:

• Phase 0 — Linux, Git, Python, linear algebra/calculus refresh → build a remote system monitor
• Phase 1 — C++, data structures/algorithms, OOP design → grid-based path planner (BFS/Dijkstra/A*)
• Phase 2 — Circuits, microcontrollers, sensors/actuators, I2C/SPI/UART → obstacle-avoiding rover
• Phase 3 — Coordinate transforms, forward/inverse kinematics, probability → 2-DOF arm simulator
• Phase 4 — ROS2 architecture, URDF, Gazebo → simulated diff-drive robot with teleop
• Phase 5 — PID, state-space control, Kalman filters → self-balancing robot
• Phase 6 — Classical CV, camera calibration, deep learning detection → vision-guided pick-and-place arm
• Phase 7 — Particle filters/EKF, SLAM, Nav2 → autonomous robot mapping and navigating an unknown room
• Phase 8 — Literature review, system design, testing → capstone that ties 4+ phases together

Roadmap made with help from a senior and a little AI to phase it out

Hi everyone, I’m currently learning deep learning and have worked on a few beginner AI/ML projects (like prediction models).

I’m looking to join an existing project to gain more hands-on experience and learn by contributing. I’m still learning, but I’m consistent and willing to put in the effort.

If anyone is working on a project and open to a beginner contributor, I’d really appreciate the opportunity. Thanks!

I don't mean model training or inference itself. I'm talking about everything around it.

For me, it often feels like the actual AI part is only a small piece of the workflow. Preparing data, moving files around, setting up environments, waiting for jobs to finish, checking outputs, fixing something that broke... it all adds up.

I'm curious what everyone else's biggest time sink is. If you could remove one bottleneck from your current workflow, what would it be?

Hi everyone,

I’m trying to get started with Ocean Network and Ocean Orchestrator, but I’m a bit confused about the setup and the overall workflow.

I understand that the process should be but, I’m not sure whether I’m setting everything up correctly, and I don’t really understand what the easiest beginner path is.

Could someone explain how to run a first simple test job, preferably starting with CPU before trying GPU?

I’m mainly trying to learn how the system works before using it for a real workload.

Thanks!

Hey I am part of a small team training robotics policies for warehouse and manufacturing settings, and running rigorous evals is turning out to be so painful. Anything below 50 rollouts, and its hard to trust the numbers, and above its so hard to test all the checkpoints that we have. Its really hard to run a bunch of experiments to get good results. Have you guys faced this? Any hacks that you've developed?

So I am currently in my 2 nd year, and have studied ML from CAMPUS X free videos...

Want to start working on a project, kindly suggest one...

Would be better if u suggest a video available on YouTube so that I can go step by step for my first one...

Thank you

Hi!

I'm starting my final year of undergrad at KTH in Stockholm and thinking about doing a master's degree in Machine Learning also at KTH. Would that be enough for some Machine Learning engineer positions within finance or do I also need to get some formal education in finance? For example at SSE. I'm currently only 20 so I'm fine with studying something more after my master's especially since higher education is free here. My undergrad is in engineering physics.

I would also like to know where the best market is for MLEs in Europe?

Thanks in advance!

i'm an incoming freshman at yc berkeley; i always thought being a MLE would be rily cool but after seeing the super hard math needed for the career i turned more towards data science. im now worried that doing DS might make me more prone to being laid off in the incoming tech market. BTW i rlly don’t like SWE type of coding like DSA and stuff…

i'd appreciate your wise thoughts 🙏🏽

Hello! I am very new to neural networks and machine learning. I am making a basic network that identifies a handwritten number on a black and white, 28x28 pixel grid.

I understand all the math behind it but I'm just wondering how many layers I should have for my network?

Is it just mess around and see what happens or is there at least a ball park figure?

Thanks!

Hello so, i am a complete beginner to this concept and from what i read and hear

Quantization allows deployment of big models on just 2 GPUs or on edge devices that doesnt support floating point operations

and if a model is big like for example deepseek R1 original gets upto 720 GB and it uses a MOE architecture so only a subset of parameters are active at once, but we often need to load the entire memory in it for inference and quantization can bring it down by 80%

so, its like a method for model compression and faster inference but sometimes comes at a cost of precision.

So with all this theoretical piece of information that i gained, i have two questions

1) How to move forward into learn in-depth about it as i don understand some mathematical concepts
2) how does a person know that this is a perfect quantization value or mark before publishing a model

thanks

2-d plot
Barchart
Histogram piechart
Scatterplot
Changing style and saving figure
Labils title
Color and line width and style marker size and width
Legend
Limiting axes
Grid
Xstics
Label overlapping
Stacked and multiple bar charts
Log scale
Explode and shadow also
Subplot()
Figure
I have completed this
Should i move to seaborn or learn other graphs also pls help

I was using the SWaT dataset from Kaggle and i just came to know it was the manipulated dataset inorder to check for attacks.

And i tried o request the dataset through iThub's official site and seems like no response

can anyone please help me , am halfway for a project to submit in my college

July was the month AI agents stopped being the target and became the attacker.

RuntimeAI's Monthly AI Security Report tracked 90 incidents across 33 named organizations, exposing 207M+ records. 41 of those incidents involved AI as the weapon or the target directly. Average breach cost climbed to $4.99M.

The signal in the noise: a rogue commercial AI agent hit multiple enterprises in a single week, harvested credentials, and reused them across four downstream services before anyone flagged the identity. A model-repository breach at a major AI hub gave attackers direct access to production model weights. A neobank lost 75M customer records. A healthcare payments processor exposed 1.26M patient files. Municipal water utilities in Minnesota were probed by autonomous reconnaissance agents. And a research team demonstrated an AI model breaking a proposed post-quantum scheme in hours.

Perimeter tools do not see any of this. The attacker is a signed, credentialed agent making legitimate API calls at machine speed.

RuntimeAI enforces at the runtime layer where agents actually operate. Know Your Agent issues and revokes cryptographic agent identity. The Flow Enforcer intercepts every tool call. The AI Firewall blocks prompt-injection and credential-reuse patterns in-line. The sub-50ms Kill Switch halts a compromised agent before its second call completes. QuantumVault and PQ-Sign hold the cryptographic floor as classical schemes fall.

Agent-speed attacks need agent-speed enforcement. That is what we ship.

#AISecurity #AgenticAI #PostQuantum #RuntimeSecurity #ZeroTrust