u/MentalFig6149

I’m a sophomore majoring in AI & Data Science, and I’m interested in getting into the remote sensing field.

I’ve started working on some basic deep learning projects, mainly image classification and before/after change detection. However, I’m struggling to figure out what problems are actually worth working on.

Are there any areas in remote sensing that are still relatively underexplored, have significant limitations, or haven’t seen much advancement yet?

Also, from the perspective of a deep learning engineer, what areas of remote sensing would be worth learning and exploring? I’m particularly interested in problems where I can apply DL rather than just using existing models.

I’d appreciate any suggestions for interesting problems, research directions, datasets, or projects that could help me explore the field seriously.

How is the BTP evaluated? Is it just marked as accepted/not accepted, or is a grade awarded based on the project and counted towards the CGPA?

I have a Sony WH-CH720N. When I connect it to my laptop using the 3.5 mm cable, the headphones work, but the microphone doesn't. Is there any way to use the headset's mic while using the wired connection?

If I use Bluetooth, the mic works, but the audio quality drops a lot (especially for hearing footsteps in games), so I'd prefer to use wired audio with the headset mic if possible.

Is this a limitation of the WH-CH720N, or is there a setting, adapter, or cable that can make the mic work over a wired connection?

I have a Sony WH-CH720N. When I connect it to my laptop using the 3.5 mm cable, the headphones work, but the microphone doesn't. Is there any way to use the headset's mic while using the wired connection?

If I use Bluetooth, the mic works, but the audio quality drops a lot (especially for hearing footsteps in games), so I'd prefer to use wired audio with the headset mic if possible.

Is this a limitation of the WH-CH720N, or is there a setting, adapter, or cable that can make the mic work over a wired connection?

I'm currently learning AI/ML ( now doing GenAI) and was wondering how much database knowledge I actually need.

Is learning SQL enough or I need to do other databases?

do I need to learn databases in depth (like someone doing backend or database engineering), or is having a good understanding of SQL, basic database concepts, and how databases are used in ML workflows enough?

one more where would you recommend learning this from? Any courses, YouTube channels, or other resources that are practical and worth the time?

Im unable to understand this, I asked AI but didn't understand much.

I have completed Deep learning till transformers & LLMs.
What should options I have next to do
I know agentic AI is a option, but is there any other option that I can try in AI field.

Any good co op FUN games I can play with 4 friend (online or through lan). Available in fitgirl.
Like PEAK, devour etc.

I have a decent understanding of Machine Learning and Deep Learning, but I've only built basic projects so far, and they aren't strong enough to showcase in my portfolio.

The problem is that most ML/DL project ideas I come across seem quite basic and simple. On the other hand, when I look at more advanced projects, they often require additional technologies and concepts such as RAG, vector databases, agents, and other LLM-related tools.

I'm struggling to find a project that's advanced enough to stand out while still being focused primarily on DL. Any suggestions?

Hi everyone,

I'm a second-year undergraduate student

So far I've studied:

• Machine Learning fundamentals
• Neural Networks (ANNs)
• Gradient Descent and Backpropagation
• Common optimizers (SGD, Adam, etc.)
• CNNs
• RNNs

I've also completed a few basic ML/DL projects.

I'm trying to understand where I currently stand (beginner, intermediate, etc.) and what the best next step would be.

Some areas I'm considering are:

• Transformers and LLMs
• Agentic AI systems
• Edge AI
• MLOps and deployment
• AI research

What are some challenging projects I can try.

what would you recommend I focus on over the next 6–12 months? Are there any important topics or skills that I'm missing?

I'm starting my journey in Edge AI and AI/ML applications for IoT. I already have a decent understanding of hardware, firmware, and IoT systems. As an ML major, I want to build expertise in Edge AI and related domains. What is the best learning path for an ML engineer to get into Edge AI, and which skills, tools, resources , and projects should I focus on?

I'm a AI DS undergrad in 2nd year and interested in IOT and embedded stuff

Please guide.

For ML-related jobs, is it necessary to grind DSA? If so, to what extent should I focus on it, or would my time be better spent strengthening my ML/DL skills?

I know the basic data structures and algorithms taught in college ( Like what are tree, sorting algorithms, data structures in C), but I haven't done any LeetCode yet. When I look at other students' profiles, many are Codeforces Masters or have solved hundreds of LeetCode problems, which makes me wonder whether I should focus on DSA as well instead of spending more time deepening my machine learning and deep learning knowledge.

I'd appreciate some guidance from people already working in ML or related fields.

I'm a 2nd year AI DS undergrad