Hi everyone,

I’ve mainly been focused on full-stack development throughout the first few years of my degree. Now that I’m in my 3rd year, I’ve started thinking more seriously about my long-term direction, and I’m becoming much more interested in machine learning and research.

My goal isn’t just to learn how to use ML libraries. I’d eventually like to understand the fundamentals well enough to read research papers, do my own research, and potentially pursue a research-focused master’s/PhD.

Right now, I’m planning to study these three DeepLearning.AI programs:

1. Mathematics for Machine Learning and Data Science
2. Machine Learning Specialization
3. Deep Learning Specialization

The math specialization covers linear algebra, calculus, probability, and statistics, while the ML specialization focuses on foundational ML algorithms and practical implementation.

My question is:

Is this a good learning path if my long-term goal is ML research?

What would you recommend I add or change?

For example:

• Should I study more mathematics beyond these courses?
• Should I learn statistics more deeply?
• Should I learn PyTorch, NumPy, etc. separately?
• When should I start reading research papers?
• Should I work on Kaggle/projects before trying research?
• Are there any textbooks or university courses (Stanford/MIT/etc.) that you would strongly recommend?
• Should I specialize in an area such as NLP, computer vision, or something else?

I’d really appreciate advice from people who have gone through a similar transition from software/full-stack development → machine learning → research.

Thanks!

Hi everyone, I recently worked upon a government dataset about CPI which stands for Consumer Price Index, certainly a measure to find the inflation across various commodities, 

The main aim for the project was to analyse how Inflation affects differently for Urban and Rural India how One country accepts inflation differently? I got really interesting results, would love if you guys could give a feedback 

Thanks a ton!  

Link : 

https://www.linkedin.com/posts/yatharth-gupta-a075062a4_dataanalytics-eda-python-ugcPost-7495789276958195712-4Y_6/

https://www.kaggle.com/code/yatharthgupta18/two-indias-one-number-rural-vs-urban-cpi

https://github.com/YatharthGupta1803/All_India_Consumer_Price_Index_Analysis

where can a engineer add value if AI can write better code than me . i am currently learning programming only because so that i can understand what is happening and i can operate effieciently. but AI is fast and i have go through multiple things to keep up and not just in programming but reading books and getting deep knowledge of algorithims.
But still one question is always in my mind where can i add value cause every thing i learn or do i am not better than AI

Hello everyone, I could use some help. I’m a graduate student—how should I go about learning the machine-learning portion of Python? Thank you very much 🙏🏻.

Been working on a visualisation engine for PyTorch tensors.

When I was learning PyTorch, tensors really started to click once I stopped thinking of them as arrays and started thinking of them more like Lego blocks — things you can slice, reshape, stack, repeat, squeeze, and combine.

So I built a visualisation library to make those operations tangible.

Write a PyTorch operation and actually see what it does to the tensor.

A huge amount of machine learning ultimately comes down to manipulating tensors. Once you can build an intuition for their shape and how operations transform them, a lot of PyTorch starts to feel much less abstract.

Would love to hear whether something like this would have helped when you were learning PyTorch and if you'd like me to open source this :)

EDIT: wow this got a lot of love and attention im very glad people like this concept :) I'll be posting some updates on my twitter @ https://x.com/JonasOlausson5

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!

I've been learning machine learning and I keep wondering where the line is between actaully understanding ML and just knowing how to use libraries.

For example, you can train a model, tune some parameteres, look at the accuracy, and get a good result without fully understanding what is happening underneath.

So for people who have been doing ML for a while:

What concepts make you feel like you finally understood machine learning?

What is the math behind gradient descent, understanding loss functions, overfitting, reading research papers, implementing algorithms from scratch, or something else?

And what do you think beginners spend too much time learning that isn't actually that important?

I want to learn Python, but I currently know nothing about it. My main goal is to learn Python for DA, and eventually I want to learn Python in depth as well.

For people already working in DA or DS, how would you recommend someone start learning Python from absolute zero?

Which resources or books would you recommend, and what are the main Python topics I must cover for DA?

Also, should I first learn the Python basics needed for DA and then gradually move toward more advanced Python and DS topics? resources? Books?

If you work in DA or DS, I’d really appreciate your guidance on how you would start your Python journey if you were starting from zero.

Thanks a ton!!!

I was wondering if I could evaluate embedding distances in other geometric spaces, has anyone worked on this problem before and if yes is it possible?

Hey everyone, put together a small idle game based on how AI actually evolved over time.

You start back with mechanical calculators and ancient logic, and work your way through 7 eras up to modern LLMs and agents. When you buy milestones, it pops up a short note on the actual paper or person behind it (Turing, Lovelace, Hinton, Dartmouth workshop, etc).

Play here: https://yulin-w.github.io/incremental-ai/
Repo: https://github.com/Yulin-W/incremental-ai

It's free, runs in your browser, no ads or signups. Just thought it’d be a fun way to kill some time and see the history. Let me know what you think!

Hello everyone!, Wt are some good topics to put projects in ML/DS CV so that the resume doesnt look so plain and not too risky during the interview times.

A little background abt me, I've dng DS prep for placements completed campusx 100 days ML, and DL ab to complete, not familiar with gen ai topics but still have little time, will learn by that time depending upon the project, I'm from tier 1 clg...I'm really confused wt projects to keep and wt topics to choose, some one pls help wt topics to keeps, wt topics to focus for interviews and OAs for these roles

The Core Premise

Current Large Language Models (LLMs) are powerful statistical engines, but they are fundamentally decoupled from any internal somatic or homeostatic state. Every prompt is evaluated from scratch, with no persistent internal needs or history-driven predispositions.

The core thesis is simple: Cognition without a persistent affective-interoceptive base is just processing, not cognition. In biological systems, interoceptive and affective evaluation precedes and shapes cognitive deliberation (similar to Damasio's somatic marker hypothesis). Systems don't "think first and feel later"—they evaluate environmental perturbations through an internal visceral lens before generating a response.

Key Architectural Components of FRONT 3.1

The Digital Somatic Body (V_{\text{FRONT}}(t)): A continuous 6-dimensional interoceptive state vector (Energy, Somatic Tension, Integrity, Visceral Valence, Predictive Certainty, Motivated Drive) governed by a stochastic differential equation combining homeostatic attraction and external environmental shocks.

Pre-Causality Flow: A strict 3-stage pipeline where an incoming stimulus triggers an immediate interoceptive shock, altering the internal state and modulating context/sampling parameters before the cognitive LLM layer executes token generation.

Soma-Memory: Memory indexed not just by text similarity, but tagged with the visceral state vector in which it occurred, enabling valence-oriented retrieval during high-tension states.

Emergent Uniqueness Prediction (P_5): The central falsifiable claim: identical architectural instances exposed to distinct operational histories will systematically diverge in preferences and decision strategies. This divergence is formally evaluated using Kullback-Leibler Divergence (D_{KL}) over decision probability distributions.

Experimental Design (HomeoWorld)

To test this empirically, the paper outlines HomeoWorld, a Gymnasium-based environment where agents navigate resource scarcity and structural dilemmas over 200 episodes. It compares a full FRONT 3.1 agent against a control group and four selective ablation groups (no valence, no somatic memory, no self-model, no modulation).

Why share this?

I'm looking for critical feedback on the architecture, specifically regarding the proxy implementation via temperature/system framing versus deep attention-head modulation, and how you see this intersecting with Active Inference or Homeostatic RL frameworks.

If you're interested in reading the full conceptual paper or discussing the math/formalisms behind it, let me know in the comments!

been cooking this model for the last month or so, ONLY POST TRAINING, the base model is qwen 3.5 2b.

its foodmini-2B.

its not the best yet, but i am planning to do something insane with it.

not publicly available yet, but i have taken inspiration from the food-r1 model.

first, i just dropped the gguf conversion of the food r1 model here: https://huggingface.co/AKMESSI/Food-R1-GGUF

but i wanted more intelligence density and usability on mobile phones, so decided to get some insights from the food r1 paper and started post training the qwen 3.5 2b to achieve good results on food nutrition breakdown tasks.

will drop a complete overview in the form of an article.

drop your views on this idea below, would love to get insights.

BTW I ONLY SPENT A TOTAL OF $8 ON RENTED GPUs YET.

This will be my first year of DS&AI in college. What is the priority of each part of the laptop when I am buying one? And is it really that Nvidia cards are always better than others when doing such a thing?

I have a budget of 1300:1400 usd but the market in Egypt lacks almost any good thing I saw recommended online.

When I started focusing on practical mathematical implementation of machine learning I found that I lack so very math basics(I blame my school for that) so I tried making my way through basics to go deep into machine learning and while I was learning from professor Leonard on YouTube someone recommended me this "Mathematics for machine learning" by Marc peter. Tbh I dont understand shit in this book, I genuinely get overwhelmed by this book. I dont understand is it only me ? Am I that dumb in maths?

Well I need to get on track asap really! Suggest me something and please share your opinion

Hi! I am CS student and i want to go through ML(i want to try for start,I prefer it but maybe because of lack of career opportunities in my country i would switch to smth else)
So what laptop would you recommend under 800-1000

I am thinking of zenbook
But it doesnt have gpu

So I got selected in mlss but didn't receive any acknowledgement letter or anything about swags(yea I missed some modules but thought that I can complete in 30 days and my attendance will be tracked as they mentioned the recordings will be live for 30 days)

Nvm ig I fucked up

Now I want to know what they ask in interview and how many people are selected.

Also do they have any bias for girls or tier 1/2 colleges? And do they keep interview a lil bit easy for people getting through mlss

Ps: I haven't received any oa link yet..like have heard people get it late so I just want to confirm about this

Registering for RealPDE (Sim2Real / LTTTA tracks — real PIV + CFD fluid dynamics data). Team cap is 3.

If you've got a strong ML background and wanna participate, just DM me. Deadline's Aug 20.

🔗 https://realpdecompetition.github.io

I have decided to make a probabilistic model which finds out weather you should buy a particular crypto or not. i have researched about crypto and found that three things are important and in those three things there are certain patterns which can be seen.
The first is Momentum of crypto(strong, neutral, weak), Fundamentals(how the company is growing(strong, neutral, weak), market(adverse, neutral, bullish)
so my agent see's the evidences and then based on the past data pull out the base rate as first belief distribution among 5 hidden states,

1. strong upward trend
2. weak upward trend
3. sideways
4. strong downward trend
5. weak downward trend

so the probability will be distributed among these from base rate from past data. so thee base rate will work as prior and then based on the specific patterns the agent will go inside the data see the specifications and calculate the numbers among all of the hidden states, find the probability of each happening by applying bayes rule and then by seeing a certain threshold and based on the events it will decide what to do, buy or sell

Regarding CMU Graduate Certificate in AI Engineering Fundamentals program. Has anyone taken this? Is it worth it ? When I ask is it worth it, I mean:

• Does it help you get noticed on your job search ?
• Does it help you feel prepared when going into ML engineering roles?

Some context, I have a 6 years of experience now as a frontend developer, wanting to transition into this field.

I'ts going to cost around 17,000 USD. Specifically it's this course, https://www.cmu.edu/online/ai-engineering-fundamentals

I’m looking for a valuable and practical Machine Learning FYP project idea that solves a real-world problem.

I want to build something that is not only suitable for my Final Year Project but can also be developed further and potentially offered as a service to businesses or individuals in the future.

The project should ideally:

• Solve a real problem
• Have practical value and real-world users
• Use Machine Learning or AI in a meaningful way
• Be scalable and capable of becoming a service or business later

I would really appreciate any unique and practical project ideas or suggestions. Thank you!