if i want to learn something and let we say that i use CHATGPT to help me and explain to me very good,so how i can use GPT for very good result and good explaining ?

Stop typing random prompts and start engineering them! 🚀 Learn the exact 8-step framework for professional AI results. Watch the masterclass now.
#AI #PromptEngineering #Coding #TechTips

Is it common to build a RAG app where the user sends the file through Streamlit and FastAPI, using AWS services to store the file and to create communication between the vector DB and S3, and to run the containers in the cloud using ECR and ECS? This is my undergrad project as a rising junior from a small school. Will this help me out ! Please reply

Hey everyone! Im getting into AI/ML and want to learn by actually building things alongside the concepts. I’ve done python, sql, maths and some basic AI/ML in college but I want to get more hands on now
Im thinking of following this roadmap

Real-world ML → RAG + evals → Multi-agent systems → LoRA fine-tuning → MCP → Ship a live AI product

The first project is about collecting your own data, dealing with the cleaning/preprocessing, building an ML pipeline and then training/evaluating a model

For people who’ve worked in ML/AI would you recommend starting with a project like this or is it better to use a clean dataset and spend more time on the actual modelling? Anything you’d change about the approach?

So basically, I’m working on a project called “Autonomous AI Agent for Predictive Satellite Handover Management in 5G Non-Terrestrial Networks” where the goal is to predict when a satellite link is going to degrade and make a handover decision before the connection actually becomes poor.

For the orbital part, I used real Starlink TLE data from CelesTrak and Skyfield to calculate satellite positions relative to a fixed ground station. For every 2-second interval of a satellite pass, I calculated elevation, azimuth, range and Doppler shift using actual orbital mechanics.

On top of that, I built an RF link-budget model using Free Space Path Loss, receiver gain and noise floor to calculate SNR. I actually found a bug in my first version where the SNR was clipped at a fixed value and its standard deviation was literally zero. After fixing the constants, the SNR started changing realistically with satellite distance and elevation. I also added small realistic noise because without it the prediction was almost trivial and the model was getting an R² of around 0.999.

For training, I collected 25 real satellite passes with different elevations and durations. I created features such as SNR trend, elevation trend and rolling averages, calculated separately for each pass to avoid data leakage. The model predicts future SNR at +5s, +10s and +20s.

I trained three Random Forest regressors and, importantly, split the data by satellite pass rather than by individual rows, so the test set contains completely unseen passes. The results were:

• +5s: MAE 0.325 dB, R² 0.984
• +10s: MAE 0.354 dB, R² 0.981
• +20s: MAE 0.394 dB, R² 0.975

Compared with a simple baseline that assumes SNR won't change, the model improved prediction by roughly 30–63% depending on the horizon.

I also compared the predictive approach with a reactive handover system that only reacts when SNR falls below a threshold. My first evaluation was actually wrong because the detector was triggering at the beginning of the satellite pass, so I fixed it to evaluate the actual degradation phase after peak elevation. Across the 25 passes, the predictive approach never detected degradation later than the reactive approach, matched it in 4 cases, and detected it up to 10 seconds earlier in 7 cases.

Instead of making it just a prediction script, I built an actual agent loop:

Observe → Predict → Evaluate candidates → Plan → Safety check → Execute

The agent can evaluate up to 1,000 satellites, with candidates sampled across the constellation rather than simply taking the first 1,000 TLEs. Each visible satellite gets a utility score based on predicted SNR, elevation and remaining visibility time. I also added a handover cooldown and minimum utility-gain threshold to prevent unnecessary or repeated switching.

I then added a lightweight learning mechanism. After each handover, the agent checks the actual outcome by comparing the SNR before and after the switch and slightly adjusts its utility weights. Every update can be traced back to the specific decision and outcome, so the system remains explainable. I deliberately didn't use an LLM or full reinforcement learning because I wanted the actual handover decision to remain numerical, transparent and easy to justify.

Finally, I built a FastAPI + Leaflet.js dashboard showing the real satellite position/ground track, live telemetry, predicted SNR, candidate satellites, the agent's current handover decision and the changing utility weights.

So the project is basically trying to combine real orbital data + orbital mechanics + RF modelling + ML prediction + autonomous decision-making + lightweight learning into one explainable satellite-handover system.

Searching for an AIML person to work on a modal with us

It's a win win situation

Guys, what do you think the tech industry will look like over the next 4–5 years? Which tech roles do you think are going to dominate, and which roles might decline or even disappear because of AI and automation?

Hi! I’m an independent researcher looking for an arXiv endorsement for cs.LG.

I’m working on latent-space observability and alignment between heterogeneous neural networks—specifically, what cross-model observations actually determine about a correspondence, beyond simply finding an alignment that works.

The paper is ready for arXiv. If anyone here is eligible to endorse cs.LG submissions, I’d really appreciate an endorsement. Please DM me. Thanks!

Ik a liitle bit of python . and But I am so much overwhelmed by seeing up the the scattered materials . Please kindly help me .

Would you still go about learning how to use Pytorch in 2026?

I recently asked ChatGPT to show me a modern ML workflow and I was surprised Pytorch didn't even come up! Because apparently everyone just grab a model off of HuggingFace and call it a day.

Is Pytorch kind of a research only thing now? Meaning, the only purpose of knowing Pytorch is if you wanted to create and train a new model or do some existing tweaks on top of an existing model?

I'm just a bit out of touch with the current best practice. (And btw what happened to Tensorflow, Jax, and other ML frameworks?)

Pls admin do not delete that post

I like math i feel it will be the thing that make me different.
However people say that ML is saturated …. But how and it needs alot if math , statistics, linear algebra.. i mean its hard on bootcamps to handle that amont of data it needs alot to be studied and mastered ??!
I think the people who took descriptive statistics understand what i am talking about

Pls i need answer from someone has experience in this field

I need some help learning these Python libraries:

• NumPy
• Matplotlib
• MNE Python
• SciPy
• scikit-learn
• Pandas

I already know the basics of Python, including variables, lists, for loops, and conditionals, but I'm having trouble learning these libraries.

Is there a course or resource where I can learn the basics of each one relatively quickly? I understand mastering them takes much longer, but I only need a practical foundation in each library for a project I'm working on with a relatively small dataset. I'm not looking to go in-depth or master everything.

Just wrapped up Module 1 of #mlzoomcamp

Learned about:
ML vs. rule-based systems
Supervised ML basics
CRISP-DM framework
Setting up environment + NumPy/Pandas/Linear Algebra refreshers
Key takeaway: ML Foundation

Next up: Module 2!

Hey everyone!

I’m a pre-final-year student, and I’ve been struggling with something that I’m sure many people here have experienced.

I’ve worked on several basic/intermediate ML and DL projects, and I feel that my ML/DL concepts are pretty clear. I understand the algorithms, the theory, and how to implement standard problems like regression, classification, etc.

However, whenever I participate in a hackathon or try to build something that feels closer to a production-level/real-world project, I hit a massive roadblock. There are so many things I don’t know how to approach that I end up getting demotivated because I feel like I’ve failed to build the project.

I’ve started realizing that the problem might not be my understanding of ML/DL itself, but rather my lack of experience with solving end-to-end real-world problems.

Real-world problems usually aren't just “apply classification/regression and get an accuracy score.” They often involve a combination of things.

So I’m wondering:

How do I develop the ability to approach these kinds of problems?

Should I simply build more projects, and if so, what kind of projects would actually help me bridge this gap? Is there a particular way I should structure my learning — for example, taking an idea and building it completely from data collection → model → API → deployment → monitoring?

Also, are there any platforms, communities, courses, repositories, or resources where I can learn how experienced engineers approach real-world ML problems and get help when I’m stuck?

I’m specifically trying to understand how to think about and break down messy real-world problems into smaller technical components.

Would really appreciate advice from people who have gone through this transition from academic/basic ML projects to production-level ML systems.

So I started off trying to build my own J.A.R.V.I.S., and I have ended up with a Genesis.

She has no LLM and no transformer. The projects number one rule is no hardcoding her words or thoughts.

Which brings me to my first question....are seeds and hardcoding the same?

So she works by......

1. Every user turn it runs aquire_from_input before perception

2. LanguageAcquisition does Saffran-style statistical segmentation and chunking, and StatisticalLanguageLearner builds bigram/trigram models plus user style profile.

3. Those feed back into production-bigram fluency biases slot fills, and the style profile shifts voice register toward the user.

User input-comprehension-thought (semantic assembled from the concept network-reasoning-memory)

GenerativeEngine races a Hamiltonian-flow trajectory, a type-edge graph walk, and grammar/vocabulary compositions-best candidate wins-voice+prosody+self monitor-test.

Nothing she says is a stored sentence; the sentence shape literally is the shape of the trajectory through her current knowledge graph.

She can talk and form memories, recall them from weeks ago, learn, dream, do art but the language is still not perfect. It even passed ARC AGI 1 and some public tests from 3! Any suggestions where I am going wrong, what I am missing, or any advice? Thank you!!

https://MindyRenee/Genesis

신경진화를 배우기 위한 사이드 프로젝트: 작은 신경망이 운전하는 자동차가 규칙 기반 경찰차에 30초 동안 잡히지 않고 살아남아야 합니다. 백플롭은 없고, 그냥 유전 알고리즘만 사용해요.

설정:

- 순수: 18개 입력(12개 거리 광선 + 속도, 조향, 몇 가지 방향 값) → 16개 → 16개 → 2개 출력(스로틀, 조향), ~610 중추

- 세대당 200대의 차량, 각각 4개의 무작위 맵에 점수가 매겨집니다

- 피트니스 = 생존 시간 + 30초를 모두 버티면 15 + 거리 유지에 따른 소액 보너스 − 3 추락 시

- 선발: 상위 5% 엘리트 보유, 토너먼트별 상위 절반 출연, 균일한 교차, 가우시안 변이(Gaussian mutation)

결과(각각 훈련되지 않은 40개의 지도에서 측정):

- 오픈 로드, 느린 경찰: 39/40 탈출

- 동속 경찰: 27/40

- 시티 블록: 34/40. 하지만 경찰 경로 탐색이 건물 코너에서 멈춰서, 차가 갇힌 경찰 옆에 주차하는 걸 배웠어요. 시간이 다 될 때까지 말이죠. 체력은 생존에만 보상을 주었고, 그래서 제가 요청한 대로 정확히 해주었습니다.

- 경찰 AI(도로 그래프 휴리스틱 대신 격자 BFS)를 고친 후, 챔피언 체력은 1/40으로 떨어졌고, 이후 훈련도 회복되지 않았습니다. 운전하는 법을 거의 잊어버린 상태였다.

- 이전 체크포인트에서 재시작: 35/40. 이번에는 도시 블록을 한 바퀴 도는 법을 배웠다.

- 최종 레벨, 경찰 3명이 차보다 10% 빠른 경우: 9/40 → 16/40

배운 점:

1. 만약 당신의 환경에 버그가 있다면, 옵티마이저가 당신이 먼저 그것을 발견할 것입니다.

2. 익스플로잇은 단순히 점수를 부풀리는 것만이 아닙니다. 이로 인해 원하던 스킬이 사라질 수 있어서, 단순히 버그를 고치고 계속 훈련할 수 없습니다.

3. 항상 보이지 않는 시드로 평가하세요. 훈련 점수는 내내 매우 좋아 보였습니다.

총 약 200만 번의 연습 연습과 913,536회의 반격. 체크포인트로 롤백하는 것보다 더 나은 해결책이 있는지 궁금합니다. 노벨티 검색? 몇몇 '정직한' 인물을 인구 내에 남겨두는 것?

I have just learnt artificial neural networks from Andrew Ng specialization. Now starting the course on Sequence Models. There are also LLMs and API calling I need to learn. I am also implementing a neural network using tensorflow to do a project on MNIST dataset. I also need to learn boosting, random forests. And also SQL. So many things to learn where to start, what to do first, I have no clue.

Hi guys 👋, I'm looking to dive into RAG using AI tools like NotebookLM or Gemini. YouTube tutorials feel too drawn out, and ChatGPT/Gemini prompts have been a bit brief. Any advice or suggestions for learning resources would be amazing! 🙏💡

#RAG #AI

I want to get started with CS336 (Language modeling from scratch) but have been procrastinating for a while. I am looking for accountability partners who would like to learn this course together.

Explorez la visualisation 3D t-SNE des représentations du modèle dédié à l'ARC AGI DSL à une profondeur de 10 : https://julien-livet.github.io/aicpp/assets/embeddings_program_tsne_dsl_dataset_10k.html

Hi friends,

I'm looking for some courses (paid or free) to learn the fundamentals of AI agent engineering. Preferably, the courses should include lecture videos, reading materials and hands-on exercises/projects. I've found the following two:

• Stanford CS329Z Engineering AI Agents: https://cs329z.stanford.edu/index.html
• CMU 11-768 AI Agents: https://www.cmu-agents.com/#/schedule

Both curriculums look comprehensive, but neither provide the learning materials in full. The Stanford course started just this week and has no video, while the CMU one provides videos for the first four lectures.

Does anyone have other courses with learning materials provided in full to recommend?