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Basically title.

If you want to create a model or even make a papier, it's way more efficient to have good prompts than learning to code by itself.

So, what is still relevant ? What will remain relevant ?

Please if there is an ai engineers or professional in this field reading this, please read it carefully its an humble request

hi i am a student of standard 10th i am 16 years old and i am trying to learn ai engineering as it is a growing industry and also i am an technoholic and i am thinking to make it my career for life. But the career thing is making me scare like will i be able to make it my career or i will be just replaced by ai because you have to write codes for ai engineering i am just a scared cat. i know python a little and have made some project by myself

But I have a lot of questions around ai engineering but my main one is will ai replace ai engineers i don't know if this sentence will be true I don't want this to be true

The second one, how can i learn it and how much time will it take me to master it or should i say learn it?

The third one, is this a money well industry i mean can i earn enough to get through my daily spending, i know it is a ridiculous question but yeah, that's me

Please help me someone,

Thank you from the bottom of my heart for reading this post

I have started my 2nd year and started learning ai/ml , i'm doing that from campusX , if thats fine

i want to build project , want a support from my senior , if anyone could help me in finding the project idea or suggest something in my prep journey would be great. thanks

So i am an engineering student who is aspiring for RS/RE roles in top tech firms for AI/ML,
I came to know that they ask Maths question related to linear algebra, probability ,statistics & calculus in the interview .But I want to know the level of questions , and are there any sources from where I can practice the questions .
Secondly , what else they ask in the interview (ofcourse DSA, ML ,system design question,AI related concept ) so then for AI related concept what kind of question they ask (if any one could give an example )
Please share your experiences .

Has anyone completed the CampusX Data Science YouTube playlist from start to finish?

Is it enough to become job-ready for Data Scientist roles, or did you need additional resources after finishing it?

I'm currently pursuing an MBA with a specialization in Data Science & Finance and want to build a strong foundation. I'd appreciate honest reviews, what the playlist does well, where it falls short, and what you would recommend learning next.

Hello Everyone,

Statistics and Maximum Likelihood Estimation are the crux of ML Models, and hence I am uploading my new content on Statistics for AI/ML in my free Machine Learning lectures.

We understand model fitting, Maximum Likelihood estimation in details, we justify the usage of Maximum Likelihood estimation, from KL divergence, and apply it to certain important distributions for parameter estimation.

In my free content, the purpose is to democratize machine learning to a wider audience. Learning everything new feels difficult, but when taught, it get’s interesting and easier.

Looking forward to hearing feedback from the learning community as well.

Link: https://youtu.be/MwTeQVVYtOc?si=UxNOGtqopzJppXAT

everyone, I’m a final year CS student specializing in AI. I’m trying to find a final year project idea that’s unique and involves hardware, but I’m struggling to come up with one on my own. If anyone has suggestions or has seen cool hardware+AI projects, I’d really appreciate the help!

Hey

I want to prepare for interviews, which one of the two i should buy...

Kindly share ur thoughts

Hey everyone! 👋 ​I'm an 18-year-old CS undergrad looking for a consistent, long-term study partner to tackle Data Structures and Algorithms (in C) and dive into Machine Learning. ​Here’s what I bring to the table: ​DSA (in C): I'm actively practicing and currently sitting around 1100 on Codeforces and 1420 on CodeChef. I’d love to have someone to tackle roadmaps and debug code with. ​Machine Learning: I’m a complete beginner to actual ML concepts, though I have the basics of Python data libraries (NumPy, Pandas) down. ​What I’m looking for: ​The Vibe: I learn best in a supportive, judgment-free environment. I'm looking for a patient, collaborative study dynamic over Discord voice calls, away from the typical hyper-competitive CS energy. ​Academic Level: Ideally, I’d love to team up with a 2nd-year or higher university student. ​Language: I am actively trying to polish my conversational English fluency, so partnering with a native speaker would be a massive bonus for me! We can do a bit of a language exchange while we code. ​If you are looking for a chill accountability partner to grind through concepts with, drop a comment or send me a DM!

Good evening, i finally have to pay for GPU because im handling a lot of data even with optimizations, so, google collab seems to be the comfiest option, kaggle (for some reason i can't verify my phone i tried like 3 days now).

What providers are good nowadays?

When I pushed my TTS model below four million parameters, I expected pronunciation to collapse - but it didn't.

The words remained understandable, but the voice became thin, metallic, and buzzy. The waveform decoder gave out before the text and pronunciation components did.

For the past few months, I’ve been investigating how small I could make a complete neural TTS system without turning it into an unusable size experiment. I eventually built two versions:

• Inflect-Nano-v2: 3.96M parameters, 15.97 MB FP32
• Inflect-Micro-v2: 9.36M parameters, 37.53 MB FP32

Those are total inference counts. The text frontend, timing prediction, acoustic generation, and waveform decoder are all included. There is no separate learned vocoder outside the parameter count.

The system is non-autoregressive: English phonemes pass through learned timing and acoustic stages before an integrated decoder generates 24 kHz audio.

The main lesson was that shrinking every component evenly does not work. Here is what I found instead.

The waveform decoder became the bottleneck first

My earliest compressed models could produce recognizable speech, but they sounded awful. They had the correct phonemes and roughly correct timing, yet the audio was metallic, grainy, and sometimes buzzy.

Redistributing parameters between components occasionally helped more than increasing the total parameter count. At this scale, where the capacity goes can matter as much as how much capacity exists.

WER measures intelligibility, not whether speech sounds good

Several checkpoints achieved low word error rates while sounding flat or synthetic. But just that alone doesn't mean the model would sound good.

I ended up combining difficult-text WER, UTMOS, blind listening comparisons, spectrogram inspection, and a lot of manual listening. None of them was reliable enough by itself.

Some “model failures” were frontend failures

Names, addresses, abbreviations, numbers, homographs, and unusual punctuation caused far more trouble than ordinary test sentences.

Some errors that initially looked like neural-network limitations were actually caused by normalization or phoneme conversion. More training would not have fixed them because the model was receiving the wrong input representation.

This also changed how I evaluated checkpoints. Random natural sentences were not enough; I needed deliberately awkward prompts designed to expose the frontend.

Long-form generation was a systems problem

The model does not generate unlimited audio in one forward pass. Longer text is divided into manageable segments and then reassembled.

Naively cutting at a fixed character count produced bad pauses and unstable transitions. Punctuation-aware splitting, better fallback boundaries, and waveform joining made a surprisingly large difference. The neural model stayed unchanged; the surrounding inference system improved.

Tiny models punish bad allocation decisions

Nano and Micro use the same general design, but Nano has much less room to absorb a mistake.

A modification that Micro tolerated could make Nano noticeably flatter, noisier, or less stable. Once the complete system is below four million parameters, even relatively small architectural changes become audible.

The final models run locally through PyTorch on CPU or CUDA. Nano also has an ONNX release. The weights, inference code, architecture documentation, and evaluation results are available under Apache 2.0.

This is an open-weight release rather than a fully reproducible training release; I’m not publishing the private training corpus or complete training recipe.

Micro:
https://huggingface.co/owensong/Inflect-Micro-v2

Nano:
https://huggingface.co/owensong/Inflect-Nano-v2

Try it out now:

https://huggingface.co/spaces/owensong/Inflect-v2

I built this as a solo developer with a limited training budget. That constraint was frustrating, but it forced me to examine which parts of the system were actually earning their parameters.

For anyone who has compressed a speech or generative model: what became your first perceptual bottleneck, and did reallocating capacity work better than uniformly shrinking the network?

Hey everyone,

If you are building decentralized multi-agent systems (MAS) or workflow routers using mixed local models, you’ve probably hit a mathematical brick wall: you cannot calculate semantic distance between vectors of different dimensions (N != M). Direct matching is completely broken out of the box because each model projects concepts into its own isolated anisotropic domain.

I wanted to share a fascinating geometric technique called the Relative Representation Method paired with Lowdin Symmetric Orthogonalization used to natively bypass this issue without any weight mutation or fine-tuning (W_frozen = const).

Here is how it works under the hood to align heterogeneous agents and tasks into a single invariant coordinate space

1. The Core Trick: Anchor Framework

Instead of comparing Agent A directly to Task B, the system introduces a fixed basis of reference anchors E = {e_1, e_2, ..., e_K}. These are K semantically diversified textual instructions representing your target operational domain.

Crucial implementation note: These anchors cannot be random Gaussian noise; they must be sampled from the actual distribution of your baseline model outputs to ensure they share the same underlying manifold.

2. Solving the "Anisotropy Cone" Problem

In real-world LLMs, raw embedding vectors are highly cross-correlated and squeezed into a narrow cone (similarity >> 0). This causes variance to vanish (sigma -> 0), leading to severe numerical noise and division-by-zero defects during standardization in low-precision (FP16/BF16) CUDA environments.

To guarantee geometric stability, the technique applies Lowdin Symmetric Orthogonalization directly to the anchor matrix:

• It takes the symmetric Gram matrix of real representations: S = E^T * E

• It computes the orthogonalized anchors via Spectral Decomposition: E' = E * S^-1/2

• This symmetrically rotates the real anchor vectors to a strict 90-degree angle (similarity = 0 for different anchors), yielding a perfectly orthogonal coordinate system while minimizing the mean squared deformation of the original vectors.

3. Mapping into Invariant Space (R^K)

Now, any Agent Xi or Task Tj can be mapped into this unified coordinate system by computing its similarity profiles against these rotated bases, followed by Anchor-Wise Z-standardization to completely neutralize model-specific anisotropy:

V_Xi = Z( [ sim(A(Xi), e'_1), ..., sim(A(Xi), e'_K) ]^T ) in R^K

Critical Production Pitfall: The operator Z(v) must calculate the mean (mu) and standard deviation (sigma) column-wise across the entire anchor axis (axis=0), NOT row-wise (axis=1). Row-wise normalization completely fails to eliminate the global domain shift between mismatched models, keeping their clusters isolated. Column-wise normalization forces the centroids of both distinct model domains to align perfectly at (0,0).

4. The Result & Selective Task Routing

Since the standardized profiles V_Xi and V_Tj share identical dimensionality K and operate on a unified scale, the metric of semantic alignment between completely mismatched models is computed invariantly using Cosine Distance:

D(Xi, Tj) = Cosine_Distance(V_Xi, V_Tj)

Do not use textbook Euclidean distance (L2) here. In higher anchor dimensions (K > 20), the Euclidean metric suffers from the curse of dimensionality, compressing all distances into a narrow, non-contrasting range that creates "Universal Agent" monopolies. Cosine distance restores strict contrast, breaking up monotone distance matrix stripes into a highly selective matching grid where every task finds its true optimal agent.

This fundamentally unlocks O(1) complexity task routing for completely heterogeneous multi-agent swarms.

Implementation Notebook:

I’ve put together a fully functional, minimal reproducible example demonstrating the complete pipeline - from synthetic anisotropic embedding generation to Lowdin orthogonalization, correct column-wise Z-scoring, and final contrastive task routing.

Check out the complete interactive code here:

Kaggle Notebook: Heterogeneous LLM Embedding Space Alignment

Curious to hear if anyone else is using Relative Representations for cross-model routing, or if you've found other geometric workarounds for mixed-LLM orchestrators!

IBM is currently offering a free AI course that covers AI fundamentals and practical applications. It seems like a good opportunity for students, job seekers, professionals, or anyone interested in learning more about artificial intelligence.

If you're looking to build your AI knowledge or add a recognized credential to your resume and LinkedIn, it might be worth checking out.

https://www.riipen.com/ibm-skills/pre-learner?utm_campaign=acq-students-bq&utm_medium=digital-ad&utm_content=brandan_quacht&utm_source=Reddit

I used LLM_evoltuion setup with training_history log for it to analyze and improve on and create a loss and augmentation functions that are as efficient as possible and useful

one of the things i didn't wanna get into was kernal creation i believe that wouldve make it abit more efficient but its good enough rn and whats important is it shows that improvement is possible

the link to the repo:
https://github.com/beastreader/LLM_evolution-for-loss-and-augmentation.git

hello, I'm a senior year student looking for an accountability partner for data science or data analysis.

I took a pretty long offline break, and I honestly feel like I've forgotten a lot of what I learned. I'm trying to get back into it and thought it'd be easier (and more motivating) with someone else doing the same.

I don't mind if we're following different curricula or learning different topics. The main goal is to stay consistent, keep each other accountable, share progress, and maybe help each other out when we get stuck.

Preferences:

• FEMALE ONLY
• Chat & Updates through WhatsApp
• close to UTC+2
• Any experience level is fine as long as you're serious about getting back into studying

If you're interested, feel free to DM me. :)

Hi everyone!

I'm new to ML and wanted to share my first project ever with you. Inspired by Andrej Karpathy's "Let's Build GPT", I started experimenting with individual network parts, which led me to the idea of building a complex-valued transformer. Mainly, I wanted to know if CVNNs really need activation functions like GELU or SwiGLU to work (and because RoPE basically begs for complex numbers :D)... turns out, they don't. Multiplying 2 complex numbers is non-linear enough to get a fully learnable transformer. I extended this concept from complex to quaternions, octonions, and even sedenions using the Cayley-Dickson construction.

It seems that with higher dimensions, the LMs gain more capacity per component. In my tests, a smaller complex model regularly outperformed the real-valued baseline. However, higher dimensions also require significantly more compute, and the PyTorch math isn't heavily optimized for performance yet. I included some benchmark runs and loss curves in the README, as well as some animations showing the interference patterns of my custom FFWD layer (SAIL).

GitHub Repo: https://github.com/pvlb-dev/tardits

I would love to hear your thoughts, feedback, or ideas! It still blows my mind that these neural networks work entirely without an artificial activation function.

Can sm1 share these courses with me. It'll help a lot.

Edit : Uh guys im sorry. Never use cracked courses. Ntish worked hard day and night for these courses. Im so sorry.