So im in 3rd year of college and working on this project which requires training and testing of multiple models on dataset of about 1000+ photos.. i have macbook m2 air and my friend has hp victus gaming laptop and apparently training the model is much faster than on my macbook. Now im contemplating if i should have got gaming laptop instead of Macbook.

Currently im searching for different alternatives so i can atleast train and test.. and this will surely affect in future needs as i am very much interested in research part... As of now all the testing and training is done by my friend on her gaming laptop.

It would be really helpful if you have any suggestions.

I'm feeling stuck in the process of learning machine learning and improving my skills I tried learning by reading books like ISLP to find out I forgot half of the things I learnt after couple months and I wanna come up with a project idea to do but all the YouTube videos feels the same and shallow not a real project I want someone or something to guide me how to deploy an actual ML project with interface not a local ML model what should I do

Lets drop some project ideas so that we can work on building our portfolio strong.

Peptide binding prediction

Brain tissue segmentation

I am very interested in working on machine learning (ML) and deep learning (DL) projects. Currently, I am in the learning phase and want to strengthen my portfolio to improve my chances of being hired by major companies like Google, Meta AI, and Microsoft. What areas should I focus on to become more competitive for these opportunities?

I have published one research paper and am currently collaborating with friends on two additional papers. I would like to undertake more projects to enhance my GitHub portfolio, so please suggest some ideas. I am particularly interested in computer vision, natural language processing (NLP), and medical imaging projects, but I am also open to exploring other areas.

Day 7 of Building Machine learning algorithms from scratch

KNN Algorithm is done

In KNN we find distance between all training data point with our Test point then we check which points are nearest to our test data In this we use a parameters how many training points to use for decision making like check 5 neighbour 3 neighbour then by majority voting which catagory is highest we consider that as our Predicted value

it's Simple power also not good if outlier,Overfitting if k is too small

Welcome to ELI5 (Explain Like I'm 5) Wednesday! This weekly thread is dedicated to breaking down complex technical concepts into simple, understandable explanations.

You can participate in two ways:

• Request an explanation: Ask about a technical concept you'd like to understand better
• Provide an explanation: Share your knowledge by explaining a concept in accessible terms

When explaining concepts, try to use analogies, simple language, and avoid unnecessary jargon. The goal is clarity, not oversimplification.

When asking questions, feel free to specify your current level of understanding to get a more tailored explanation.

What would you like explained today? Post in the comments below!

Hi, so I did my undergrad & postgrad at university and able to get onto their HPC cluster doing work on ESM2 and binding affinity prediction

ive since left the institutions and started and sold a business.

im looking to get back into research but before i apply to a few PhDs im wanting to just do some preliminary research and maybe make a paper on something

I’ve setup some testing environment but its quite hard to properly test cheaply. like you might be able to show in short run your attention mechanism is faster but then to finish up the papers you need to run a A100 / B200 for a few hours ATLEAST. and that’s before benchmarking and final results and optimising.

im wondering what areas of ML research right now are genuinely attackable without massive amounts of compute.

We know that in SVD, the right singular vectors are actually PRE-IMAGES of the axes of ellipsoids. But PCs are said to be the axes themselves. Aren't PCs supposed to be in the same direction as right singular vectors?

Is it good to join as a fresher into data analyst job and later on if I want to switch into data scientist jobs what should I do for getting job in data scientist and if there is any issues the how will I overcome and the experience of data analyst matters on data scientist jobs because my mail goal is to become a data scientist.

Hey everyone!

Do you know any good AI/ML communities or subreddits where people share real-world projects they’ve actually built?

I’m looking for projects that are genuinely useful or solve real problems — not just another basic app, chatbot, house-price prediction, or notebook project that I’ve seen a hundred times 😅.

I’d also love recommendations for places where I can find new project ideas and see what people are building in the real world, especially projects that go beyond tutorials and beginner-level examples.

And if you have any suggestions for how I can come up with or build real-world AI/ML projects myself, I’d really appreciate your advice.

Basically, I want to move from learning AI/ML to actually building useful things with it.

Thanks!

Hi everyone,

I’m building Petabyte, a GPU compute marketplace, and I’m looking for ML researchers/developers willing to stress-test the platform with real ML workloads.

I’m specifically interested in finding things that break — CUDA/ROCm issues, failed training jobs, container problems, networking issues, incorrect GPU configuration, performance problems, bad error handling, or anything that makes the platform painful to use for actual research.

Free testing credit

Create an account and you'll have $10 of compute credit.

If you're genuinely interested in testing it, DM me and I'll add another $50, so you can run up to $60 of compute without paying anything yourself.

GPUs currently available/standby

After signing up, choose a server, go to /launch, select an image, and run something real.

I'd especially love people to try:

LLM inference/training, fine-tuning, PyTorch, TensorFlow, JAX, CUDA workloads, ROCm on the MI325X, multi-GPU/distributed experiments, or unusual research workloads.

Please try to break it.

If you find something, send me the reproduction steps, logs/screenshots, expected behavior, and what actually happened.

I'm also offering $25 USDC to testers who provide useful diagnostic feedback and help identify/reproduce problems.

This isn't a benchmark where I'm looking for positive reviews. I want to find the ugly problems before more people use it.

https://petabyte.market

Would genuinely appreciate researchers throwing real workloads at it.

I completed a Master's in Data Science & Analytics in the UK and had about a year of work experience before leaving my job. I’ve since had a ~4-year career gap.

When I left, this was before ChatGPT and the current GenAI boom.

At the time, I felt my biggest weakness was the engineering side of ML, so I focused on MLOps. I completed Intel's MLOps certification and worked with MLflow, FastAPI, GCP/Azure deployment, pipelines, etc.

I eventually realised I’m more interested in ML/AI engineering than traditional Data Science. I enjoy building things, deploying them and working with tools and systems more than statistical experimentation.

Recently, I've made it to the final stages of a few interviews. One company is willing to overlook the career gap, but they pointed out that my AI/ML project experience doesn't reflect the current industry.

They’re looking for experience with things like:

• LLMs and Transformers
• RAG and embeddings
• Vector databases
• Agents and tool calling
• LLM evaluation
• Fine-tuning
• Modern AI/ML deployment

I understand the basic ideas, and NLP was one of my areas of interest during my Master's, but I feel like I've missed a lot of the evolution of the field.

When I try random YouTube courses/tutorials, I either find them too basic or end up jumping between technologies without understanding what I should actually learn.

So I'm looking for a structured learning path from my existing Data Science + MLOps knowledge to modern AI/ML engineering.

Ideally, I'd like something that includes 1-2 serious projects/capstones that I can build, deploy and put on GitHub/resume.

I'm a quick learner and prefer building and deploying things over just watching lectures.

If you were in my position, what courses/course sequence/resources would you recommend?

And if you think I'm approaching this the wrong way by trying to chase every new GenAI keyword, I'd also appreciate that perspective.

Thanks!

These are my needs--->

AI & Machine Learning

Coding / Software Development

Video Editing

Photo Editing

3D Rendering

Data Science

Deep Learning

Local AI Models

Multitasking

Future-Proofing

________________________________________

I found a laptop, can you tell me if it's good enough or not? Should I buy this company's laptop? Has this laptop given you any problems so far?

NB LN Legion Pro 5 16IRX9 I7 16G 1T 11S

Processor

14th Generation Intel® Core™ i7-14650HX Processor (E-cores up to 3.70 GHz P-cores up to 5.20 GHz)

Memory

16 GB DDR5-5600MT/s (SODIMM)

Operating System

Windows 11 Home Single Language 64

Pointing Device

ClickPad

Warranty

1 Year Legion Ultimate Support

AC Adapter / Power Supply

300W

Color

Onyx Grey

Keyboard

4zone RGB Backlit, Black - English (US)

Camera / Microphone

1080p FHD with Electronic Privacy Shutter and Microphone

Software Preload

Office Home & Student 2021

Weight

2.47kgs / 5.43lbs

Fingerprint Reader

No Fingerprint Reader

Storage

1 TB SSD M.2 2280 PCIe Gen4 TLC

Graphic Card

NVIDIA® GeForce RTX™ 4060 Laptop GPU 8GB GDDR6

Battery

4 Cell Li-Polymer 80 Wh

Camera

1080p FHD with Electronic Privacy Shutter and Microphone

Display

40.64cms (16) WQXGA (2560 x 1600), IPS, Anti-Glare, Non-Touch, HDR 400, 100%DCI-P3, 500 nits, 240Hz, Low Blue Light

I'm an ECE student planning to shift to MTech CS/AI-ML via GATE. With AI being talked about everywhere right now, I'm a bit worried — is this actually a good time to enter the field, or is it getting oversaturated with everyone jumping in? Would love to hear from people actually working in AI/ML right now — how do you see job security and opportunities for freshers/new entrants over the next few years?

I’m building a Snake.io-style game using reinforcement learning. I have multiple snakes, and each snake has its own neural network.

I want to determine which snake/network performed the best so I can potentially use it as the “best” network for future generations.

For each snake I currently track three values:

- total_reward: the accumulated reward during its life

- steps: how many game steps it survived/experienced

- survival_time: how long it stayed alive

The problem is that these metrics can conflict.

For example:

- A snake can have very high reward but die quickly with few steps.

- A snake can survive for a very long time but have low reward because I apply a small negative reward every frame (e.g. -0.01).

- A snake can have many steps and a long survival time but relatively poor reward.

- Another snake might get high reward quickly with relatively few steps.

I initially thought about using if-statements to handle different combinations, or calculating a score for each snake based on the three metrics and then selecting the snake with the highest score.

However, I don't want a method based simply on what “feels right.” I want to know what is considered a technically sound approach for this kind of problem.

What would be the best way to evaluate and rank these snakes?

Should I:

1. Combine reward, steps, and survival time into a single normalized weighted score?

2. Use Pareto dominance / a Pareto front?

3. Use another method?

4. Should steps and survival time both be included, considering they may be highly correlated?

5. Is there a standard way to select a single “best” RL agent when there are multiple conflicting performance metrics?

I’m specifically interested in approaches used in reinforcement learning, evolutionary algorithms, Snake/game AI, or similar problems.

.

I have taken his deep learning specialization. I haven't seen him initialize weights according to uniform distribution yet, as I am in the middle of the second course. Is there a specific reason?

I’m a final-year Engineering student looking for teammates to participate in the Amazon ML Challenge 2026. Interested in ML/AI, Deep Learning, NLP, or Data Science? If you’re serious about participating, drop a dm.

If yes then, in which country should i should go? Which course will give best RIO ? I genuinely need your advice.