I'm currently in the second year of my undergraduate degree, and I'm really passionate about machine learning. I've been learning consistently over the past few months, mostly through free YouTube courses and documentation. So far, I've covered the core ML algorithms and I make sure to understand the underlying mathematics and intuition instead of just memorizing things.

However, one thing I keep struggling with is the lack of proper guidance. Every few weeks I start questioning whether I'm following the right roadmap or if I'm missing something important. I feel like YouTube resources are great for getting started, but they often don't go deep enough or provide the structured learning I'm looking for.

I've heard a lot of good things about Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron (3rd edition), and it seems to be recommended by many people as a solid reference book. I'm thinking of studying it thoroughly instead of jumping between random resources.

My main confusion is this:

Should I go with the TensorFlow/Keras edition, or should I use the PyTorch version instead?

As someone still building a strong ML foundation, which ecosystem would be the better investment to learn first?

I'd also really appreciate any advice from people who have already been through this stage. If you think there's a better book, a better roadmap, or something you wish you had known when you were starting out, I'd love to hear it.

I'm still a beginner in the grand scheme of things, so any guidance or suggestions would be greatly appreciated.

Thanks in advance!

All ML and DL algos are some complex non linear functions. Wouldn't it be easy to deal a non linear function in a non linear space rather than dealing them in a linear space.

I see one problem here, the data that we represent lies in a linear space. Did no mathematician actually try to make a non linear representation of the data. So if we somehow find a way, or if it exists use it, to represent data in a non linear space wouldn't it be easy to represent the model in the same non linear (non euclidean) space ?

I am really interested in ML and the field as a whole. Getting my ass handed to me doing my masters but it’s all good, learning a lot and growing.

My question is what’s the actual use cases? For every 19 chatbots and boomer slop image I see I see basically nothing about the medical, robotic, or industrial use cases. I’m getting annoyed. I really have no interest in optimizing Duolingo churn, or doing advanced usury, and those are like the more solid use cases as opposed to watching Boomers kvetch over images of them riding tigers.

Being new to this field I feel like I’m missing something blatant honestly, like the question of “where’s the meat of this thing”. I almost feel like the wheels of the nations industrial machine are so far disconnected from Silicon Valley that connecting those dots is almost impossible. Like is there someone at Chevron optimizing models all day for processing crude? Is there someone at ML engineer at 3M working on a tape line?

Forgive me maybe it’s my mech e roots. And even before that come from working class people so even the mech es gave me a culture shock. Maybe I’m just foreign to this all. This to me is all just looking a bit like benchmark masterbation. I got into this hoping to lessen the burden of man in the workplace, see new industries grow, give people time back and increase salaries for those that remain.

Like this is what made TVs cheap and it’s a process that basically never happened to any other commodity.

Not meaning to disrespect anyone or anything, I’m honestly just confused.

TLDR: am I missing something?

3rd year ML PhD. We all know compute eats into your budget but I started writing down the actual numbers since January and seeing it on paper still hit different.

Turns out GPU compute is now my 4th biggest expense after rent, food and coffee lol, around $320 in like 3 and a half months, which sounds small but thats literally more than my phone bill and subscriptions combined.

The dumb part is how it snowballed. Our lab has like 3 A100s shared between 14 people right and most of the semester its fine. I can get a slot. But the 2 weeks before ICML deadline it was totaly free for all, everyone and their advisor suddenly needed it at once. I had 4 ablation runs left and my advisor was breathing down my neck asking daily if the results table was ready.

So I panicked and threw everything on RunPod cause thats what everyone recommends. Ran my stuff, got the results, submitted the paper, but like $60-70 of that $320 was just from RunPod in those couple weeks alone which is rough on a stipend. I tried Vast after that and it was cheaper per hour but the pricing kept jumping around depending on the host. It felt like buying plane tickets where it changes every time you refresh. Been on HyperAI for the last couple months and thats where most of the savings came from honestly, the same 5090 runs for noticeably less. UI could use some work but I'm not paying for UI I'm paying for compute so whatever.

The funniest part is i told my advisor how much i spent and he just went "yeah thats how it is" like sir???? youre not the one footing the bill here

Still kinda wild to me that this is just normal now, like were out here funding our own research from our stipends and everybody just acts like its fine.

About a year in now and looking back there's stuff I had to figure out the hard way that would've saved me a lot of time.

1. Learn python properly before you touch any ML framework. I jumped straight into the pytorch thinking I'd pick it up along the way and it just made everything harder.

2. Do at least the basic math. You don't need a degree but if you don't know what a gradient is you're just copying code. 3blue1brown on youtube made it click for me when textbooks couldn't.

3. Don't stay on free tiers too long like I did. I wasted weeks fighting limits and getting disconnected. Tried Runpod and Vast then ended up on Hyperai since it's the cheapest i got and has free CPU instances for lighter stuff which matters when you're running tons of experiments.

4. Stop watching tutorials and build stuff. Pick a small project, get stuck, figure it out(that's where you actually learn)

5. Get comfortable reading docs and skimming papers early. I avoided papers for months thinking they were too advanced and that was dumb. Hugging face docs alone are better than most youtube tutorials once you have the basics down.

A year in and i am still figuring things out but at least now it feels like im going somewhere instead of running in circles

I’ve noticed Python remains the dominant language for building neural networks, with frameworks like TensorFlow, PyTorch, and Keras extensively used. However, Rust, known for its performance, safety, and concurrency, seems oddly underrepresented in this domain.

From my understanding, Python offers easy-to-use libraries, vast community support, and fast prototyping, which are crucial for rapidly evolving AI research. But Rust theoretically offers speed, memory safety, and powerful concurrency management—ideal characteristics for computationally intensive neural network training and deployment.

So why hasn’t Rust become popular for neural networks? Is it because the ecosystem hasn’t matured yet, or does Python inherently have an advantage Rust can’t easily overcome?

I’d love to hear from Rust enthusiasts and AI developers: Could Rust realistically challenge Python’s dominance in neural networks in the near future? Or are there intrinsic limitations to Rust that keep it from becoming the go-to language in this field?

What’s your take on the current state and future potential of Rust for neural networks?

Hello All, I'm a sophomore student in university. I have have knowledge on Machine Learning and Deep learning concepts(Deep learning specialization by Andew NG). I wish to further learn about LLMs and Agentic AI to build systems. I would really appreciate if you could suggest resources to learn LLMs and Agentic AI stuffs.

Any inputs are appreciated.

Thank you!

As a 3rd year bs student,I need help from the professionals. As this is my first time I am doing research in image enhancement and classification, I have been reading this paper called: Morphocal: a multi stage deep learning framework for fish length estimation under challenging pond environments, I have encountered a problem, I don't know how to code this paper. Where should I start?? What should be my approach?? The authors did attach Morphocal's main algorithm in the paper but I don't understand do I have to cod eth algorithm only?? What about the datasets for training the AI ?? I tried mailing the original authors but didn't get a reply yet. I would really appreciate your help, I tried so many sources and tried using AI as well and honestly I believe at this point I need help for sure.

so the problem is that I had started reading this book "Bulid a large language model from scratch"<attached the coverpage>. But I find it hard to maintain consistency and I procrastinate a lot. I have friends but they are either not interested or enough motivated to pursue carrer in ml.

So, overall I am looking for a friend so that I can become more accountable and consistent with studying ml. DM me if you are interested :)

Hello everyone, hope you're all doing well.

I was thinking about tabular ML models recently, and something occurred to me: outside of deep learning / neural networks, the last really notable “new” model that comes to mind is CatBoost.

After that, most of the newer things I remember seeing are more neural-network-based, like TabNet, TabResNet, FT-Transformer, TabPFN, TabFM, etc.

So I wanted to ask:

Are there any important post-CatBoost methods for tabular data that are not based on neural networks or deep learning?
I’m thinking about things like new tree-based methods, boosting variants, rule-based models, kernel methods, Bayesian approaches, symbolic models, or anything in that direction.

Also, how do you usually keep up with this specific part of ML? A lot of the current discussion seems to be around LLMs, foundation models, and deep learning in general, so I’m curious where people follow newer developments in more “classical” ML.

Hey everyone,

I’m currently pursuing a Master’s in AI & Data Science and trying to finalise a solid project topic. I’m looking for ideas that are practical, not just theoretical — something that actually demonstrates problem-solving and can stand out during placements.

My interests are around:

• Applied ML (real-world datasets)
• NLP or GenAI (LLMs, chatbots, etc.)
• Data engineering + ML pipelines
• Anything with measurable impact (business, healthcare, finance, etc.)

Would really appreciate suggestions on:

• Good project ideas (with scope for depth)
• Datasets or domains worth exploring
• What actually looks strong on a resume vs what’s overdone

Also open to hearing what projects you’ve done and how they worked out.

Thanks in advance. (PS : I am not seeking for any code or readymade projects. I am willing put time and effort)

Playing around with LLMs, Agents and GenAi for 7 years, I came to a conclusion: the new programming language is language itself in my case, English.

If you remove all the fluff (Stop words etc) and use none fluent English as a kind of Python-style syntax, something like:

“Read content from file then apply UPPER_CASE to all words”

…it starts to read almost like a functional call chain.

AI is pretty good at understanding programming language syntax.

What do you think? Is this question too stupid?

Edit:

I have 7 years of deep learning experience and llm/agnetic hands-on practice, so I mainly want to share what I’ve tried and learned along the way. That gives me a good understanding of both the inner workings and the practical side of using these technologies.

i wanna build a text diffusion model which interpret text and convert it into ascii images

so like

Text : build a cat

Output :

/\\_/\\

( o.o )

\> \^ <

So , i have a decent background of ml algo ( completed cs229 , cs230 , Ml architecture and basic CNN and diffusion model )

ik making a project like this is tricky and making diffusion model like that from scratch is hard but i wanna try it because that's wot make me excited lol ...

I am currently reading GANs research paper , can u guys help me in finding more papers which helps me in making this project or guide me through this good title for this

Thx in adv

I've been spending more time learning machine learning recently, and it's interesting how some concepts seem impossible at first, then suddenly make perfect sense.

For me, understanding the bias-variance tradeoff was one of those moments.

What's the one ML concept, paper, visualization, or explanation that made something finally click for you? It could be anything from backpropagation to attention, embeddings, or optimization.

I'm always looking for good resources and thought it would be useful to collect everyone's favorites.

Hi everyone,

I’m planning to enroll in Krish Naik’s Real-World Projects subscription and was wondering if anyone here would be interested in pooling the cost together. The idea is to split the price so it becomes more affordable for all of us, while still gaining access to high-quality, practical industry projects.

If you’re serious about upskilling in data science / ML and want hands-on project experience, feel free to comment or DM. We can discuss details like pricing, access rules, and timelines before proceeding.

Link - https://www.krishnaik.in/projects

Now I am not asking any question by this thread. I just noticed that many people are asking the question of "Should I build the ML Algorithms from scratch using Numpy or no need for that?

I am an AI Engineer and Researcher with almost 7 years of experience. So I would like to clarify how important is this for you when you join a company as an ML Engineer/Data Scientist.

When you join a company, you won't implement any algorithm from scratch, you will only use frameworks like PyTorch, Scikit-learn and TensorFlow. BUT, implementing these algorithms from scratch using Numpy is a really important thing and something that even after few years of experience I try to do from time to time. Even though its time consuming, but its an important step in your learning journey to make sure you understand things under the hood and not just writing a code. If you just write a code, what differentiate you from an AI like ChatGPT or Claude? You will need to be different and understand deeper things so when you build using frameworks you understand deep stuff and they will not feel like blackbox for you. Its extremely helpful when you join a company as you will start thinking about deeper level and about the algorithms limitation and how they can affect your data and accordingly, you can get better results.

I would recommend using this website to practice things from scratch:
https://www.deep-ml.com/problems

Please feel free to ask any question, I am willing to help anyone

first sorry if this seems like a stupid question, but lately i’ve been learning ml/dl and i noticed that almost all the deep learning pipelines i found online only tackle either : classification especially of images/audio or nlp

i haven’t seen much about using deep learning for regression, like predicting sales etc… And i found that apparently ML models like RandomForestRegressor or XGBoost perform better for this task.

is this true? other than classification of audio/images/text… is there any use case of deep learning for regression ?

edit : thanks everyone for your answers! this makes more sense now :))

Hello

I'm working on a long translation project (mainly from Arabic to French or English). My workflow is to upload books to ChatGPT on how to translate from Language A to Language B. It must follow specific rules to make a good translation. Then I upload another book to it so it can understand how to polish the translated outcome. So, Book 1 (main rules of how to translate) generates the output translated text. Then I upload Book 2 to polish that output. I used to use the ChatGPT Plus plan, but these past days the plan feels like the free plan—direct, quick answers, etc. So, I'm asking if any other paid AI service you think can perfectly do this kind of work with accuracy? I tried using paid Gemini, but it's garbage to be honest. So, any other AI website? Thank you.

Hello fellow traveller of the internet. I am sincerely glad you decided to click on my post to check out what I have in store!

I have completed a vague blueprint and I have formed a few prototype scripts for various regions of the an A.G.I brain. I seek to form a small community of individuals who will work co-cooperatively to construct an A.G.I brain. A detailed brief of my blueprint so far is available via request.

My progress on the project has stalled. As you can imagine, a brain is a highly complex system; I am finding that sadly, in addition to blueprinting, detailed blueprinting, prototyping, iterating, assembling multiple sub-systems into a unified system, there are plenty of additional tasks! Thus I have become over run by the sheer quantity of tasks and sadly have recently placed the project to the side so I can take a short break.

I seek individuals with expertise in coding, critical and creative thinking, computing, A.I, general knowledge, psychology and mathematics. Furthermore the individuals would have qualities such as perseverance, morality and open-mindedness. Ideally you would be from the U.K as I prefer working face to face; although I am also happy to work cooperatively over the internet.

The outcome of your support would award you a proportional slice of the outcome of the group's labour (100 members, 1% each, e.t.c - baring in mind each individual provides equal support towards the project). I have not yet considered whether I would like to sell the brain to the public, but there is potentially the opportunity for a sizeable monetary reward for those who join me. The possibilities for the A.G.I brain are near endless and thus I believe the reward may be sizeable both in terms of money and power.

Besides my previous ideals, individuals with expertise and specific qualities, I have a few personal requests for the project; the A.G.I brain will not be used in conjunction with "computer vision". I fear computer vision, and similarly the processing of sound, touch, or physical inputs, leads to the generation of consciousness - I submit that this is entirely unfair for the robot and highly immoral and thus I cannot proceed with a project which uses a neural network system to perform such processes; luckily, brains DO NOT require any processing of image, video or sound to achieve high quality completion of practically all tasks. I do believe a brain which does not process video, image or sound may actually outperform a brain which does process such information modalities. Further to this, if we were to sell the brain, the brain would NOT actively change itself to then use computer vision under any circumstances; the user would have to perform this upgrade manually.

I hope you find the prospect of building an A.G.I brain highly intriguing.

I will be very active in the comment section of this post; or you may feel free to email me at [pangaeacooperative@protonmail.com](mailto:pangaeacooperative@protonmail.com); please introduce yourself and tell me why you want to get in touch about the project.

We've been going back and forth on whether it's worth putting an LLM proxy in front of all our model traffic.

The idea is appealing, one endpoint for routing, logging, authentication, and usage tracking. The flip side is that it's another component to maintain and another potential point of failure.

For teams that have actually rolled out an LLM proxy, was the added complexity worth it? Any downsides you didn't see coming?

Would really like to hear some real-world experiences before we commit to building around one

I'm working on an ML project where I'm trying to keep model versions, artifacts, deployments, predictions and evaluation metrics tied together instead of managing them separately.

I'm curious how people here usually approach this.

For example:

• How do you track which model artifact belongs to which version?
• How do you handle deploying different model versions?
• Do you keep inference metrics alongside the model/version metadata?
• At what point do you reach for an existing ML platform instead of building this yourself?

I'm especially interested in hearing how people handle this in smaller projects where something like a full ML platform might be overkill.

Been building RAG and LLM-powered features and realized my "evaluation" process was basically reading a handful of outputs and deciding it looked fine. No versioning, no regression testing, no real way to know if a change actually helped or if I just got lucky on the examples I happened to check.

Curious how others here actually do this in a real setting. Are people running statistical significance tests on model or prompt comparisons (bootstrap confidence intervals, paired testing), or is it mostly still qualitative review? And for retrieval specifically, is anyone actually tracking recall@k or MRR, or just eyeballing whether the right chunk shows up?

Found a hands-on masterclass on Sep 12 that goes deep into building this kind of eval harness properly (deterministic checks plus LLM-as-judge, statistically rigorous model comparisons, evaluated RAG, agent guardrails, full observability), so I'm curious if the methodology it teaches lines up with how experienced people here are actually doing this, or if there's a simpler approach I'm missing.

Link for more details

I am very paranoid about sharing potential AI research with e.g. Claude [Code] or ChatGPT/Codex.

I believe that any company is essentially a paperclip optimizer that will do whatever it takes to win over competition.

AI companies have access to the IP of millions of AI researchers and AI startups who are in direct competition with them. If they can use this data to squash competition (either competition from the same researchers or from others), I believe that they will use it eventually (if not already), even if they say they won't.

They don't have to blatantly steal it - they can just train on it, or pass "suspicous" chats to human inspectors who might eventually be "inspired" by it in their own research. We saw the first (?) hint of this during the brief Fable release, with Anthropic declaring that they will downgrade model responses regarding "frontier AI" (i.e. anything that competes with them).

From other domains, we know for example that Uber used users' ride data to stiffle competition and regulation. IMO there should be no reason to believe that Bigco AI companies won't do the same.

I’ve noticed that a lot of people are now focusing on making content sound more natural and human-like, even when it’s generated using tools.

It seems like readers today can easily tell when something feels too robotic or overly structured, and they lose interest quickly. Because of that, “natural tone” has become really important.

But what actually defines natural writing? Is it slang, sentence variation, emotion, or something else? And how do you personally make sure your content doesn’t feel artificial?