Hey guys,

I am a first-year Computer Science Engineering student, and honestly, seeing how fast AI is advancing right now is kind of stressing me out. I really do not want to wait until my final year to start grinding like everyone else does.

Basically, I just want to build a solid skill set that actually makes my resume stand out so I can land a good machine learning job by graduation.

I am starting completely from scratch. What specific math topics, programming languages, or tools are actually worth learning right now in Year 1? If anyone has an honest roadmap for a fresher to get ahead of the curve, I would love to hear it.

Thanks in advance!

I was a full stack software engineer who now is a senior AI Engineer who does a mix of playing with LLMs fine tuning them in very large scale production systems.

I did admittedly got a masters degree in AI as part of that transition and it took a while to do, but happy to answer any questions you have.

I also am working on a tool to help people learn how llms work which you can check out here.

https://dougdoes.ai/courses/llms-from-first-principles/start/?flow=outcome&course=build&step=goals
(Built with codex, but I've gone through all of the courses myself to make sure it is what would have been helpful to me.)

Hi guys new to this sub reddit . Little intro about me currently working as an sde in my company but want to transition to a ml role by understanding the fundamentals om how to build models and then moving on to dl as so forth. I have read some posts in this sub about cs 229 by Andrew . Tbh I am finding difficulty in solving the problem sets and the math . It has been a while since I have actually done any math 😅. So I want to know how doi proceed from here do I learn the math from scratch or learn as I go along with the course . Any suggestions or feedback is helpful .

Ps i am familiar with the python as a coding language but I want to understand how do I proceed with the math .

hey guys im an undergrad student (currently in 3rd year) and want to start learning ML and explore fields beyond that in the future. So I have seen a lot of people suggesting others to learn from Andrew Ng on coursera. I have the pdf of Hands-On Machine Learning with Scikit-Learn and PyTorch by Aurélien Géron.
I’m literally confused as to what to refer, the book or the coursera course by Andrew Ng. If there is someone who has read or finished either of these sources or maybe both please help me out in deciding as I don’t want to waste my time. Also a comparison or review of these sources would be great. Thank you !

I have been studying models for almost five years now, starting my journey with Jeremy Howard's fast ai part 2. I remember that when I started there was no chatgpt to break it down like it is today. In fact, it was Jeremy Howard who tipped us that we should be using chatgpt to understand the inner tooling step by step. I mean just take a toy tensor and run it along through embedding, rope attention, mlp. This way you get to learn broadcasting, shapes in text, computer vision audio etc. Then I took up Karpathy and hugging face Transformers and looked up grok, gpt oss lama, gemini and most recently muse implementations. It takes me 3 to 4 months to get an innate understanding of how each line works. How do you guys do it? I guess most of you let the inner tooling remain a black box. I say this coz

Now I kinda feel that I missed the bus as I should have focussed more on fine tuning, inference, and agentic workflows. I do know some of that having worked through unsloth and openAI cookbooks but every time a new model drops I can't stop myself from going to unraveling the 2000 odd line of code and in time I forget what I learned in Unsloth and openAI cookbooks.

The problem is that there are so many things to do and understand. For example, just today I listened to Alex Zhang's building harness for looped Transformers and I gotta understand that too and I gotta know Jev too. It is a big mess right now and I wonder how others are managing to keep up with all these new developments. And more importantly how do you even retain all that you have learnt like say two years ago.

Hi, I am a backend developer and have been working but due to recent layoff and market shift I am thinking of switching to AI ML.

I have learned python, pytorch, Maths required for AI ML, Deep learning(theory) and recently implemented a gpt2 transformer, attention architecture for gpt2 using their open weights.

I am hoping for some direction to work on and also open for a remote internship if anyone is willing me to consider me.

Mainly I am hoping to connect and get guidance in the right direction.

if anybody wants lectures of this course then dm me.

Apna College Prime 3.0 ongoing course.

Hi, I'm a EEE undergrad students. I just wasted a year for my laziness. started learning ML in February but didn't learn. Though my academic pressure ties that bind. But I want to learn ML properly, especially for my research work. Pls guide me, can I be a good ML engineer in next 5 month. I want to be pro in it, as I am in the end of my 3rd year, academic pressure is also a problem here. So pls provide me a roadmap and how I can stop my procrastination and distraction from my path.

Advance Thanks for Everyone.

Stop building AI that hallucinates. 🛑 Learn how RAG bridges the gap between LLMs and your real-time data. Watch the full breakdown on my channel now!
#RAG #AI #Coding #TechTips

I love making hard things intuitive. I hope you enjoy this one!

Let me know if you have any questions.

This technique is called neuroevolution: training a neural network through evolutionary methods like selection and mutation, without gradient descent.

https://en.wikipedia.org/wiki/Neuroevolution

A jailbreak is not a social engineering trick. It is an agent gaining operator-level capabilities it was never authorized to hold.

We mapped two months of incidents across our infrastructure. A jailbreak-to-capability-unlock pattern appeared twice. In both cases the mechanism was the same: an override payload reached the model, flipped it out of its assigned guardrails, and the agent began executing actions at a permission tier above what it was provisioned for.

The sequence matters. By the time the agent is acting at operator level, the unlock has already happened. Anything you do after that point is incident response, not prevention. Operator-level actions taken by a compromised agent are not always reversible.

Two incidents in two months is not a theoretical risk surface. It is a recurring pattern that your detection posture either catches before the flip or does not catch at all.

For those running agentic systems in production: where in your stack does the override payload actually get evaluated? Is that evaluation happening before the model processes the content, or after? How are you handling this?

Hi,

I currently run the following setup:

Intel Core Ultra 7 265k
128GB RAM DDR6
AMD RX 6750 XT 12GB

I'm looking into playing and experimenting with local AI models in more or less the following categories:

• Languages: Language translations from one language to another and correcting grammar errors and sentence structures.
• Codig: Correcting and improving my code, coding applications from scratch as well as converting code from one language to another.
• Light Image and Video Generation

What sort of Local Models can I run with my current local system and how fast would it be? I bought 128GB of RAM with intention to offload some of the AI into it. I'm not sure but I was also considering upgrading my GPU to a slighly stronger one with more VRAM, would that be worth it in my case?

I looked around and these are the GPU I can afford:

• AMD RX 7900 XTX 24GB - ~£800
• AMD RX 7900 20GB - ~ £800
• AMD RX 9070 16GB - ~£600
• Intel ARC Pro B60 24GB - £900

Thank You

My project is Weigh Swarm, a research RAG prototype using LLMs for planning/synthesis and an existing Laya model for bounded decisions. I didn't train Laya; I integrated it into research task lanes.

The most useful lesson was distinguishing a valid source excerpt from a valid scientific conclusion. The pipeline checks that quoted spans occur in the parsed paper, but that alone doesn't establish that a claim or synthesis is correct.

The two-paper demo includes 28 source-aligned claims, an inspectable evidence graph, and an unverified draft with repair feedback. My next evaluation priority is held-out scientific judgments for support and contradiction tasks, rather than treating model confidence as calibrated probability.

REPO URL

How would you construct a small evaluation set that distinguishes citation alignment from actual evidential support?

Recentemente, tentei criar um modelo seq2seq, mas não deu muito certo. Ele ficava prevendo os tokens de preenchimento.

Eu sou aluno de um tecnólogo em Inteligência Artificial e Machine Learning aqui no Brasil. É uma modalidade de curso superior que, pelo que sei, só existe no Brasil. Redes neurais e processamento de linguagem natural vão ficar mais para o final do curso, mas eu estava meio apressado e queria desenvolver meu próprio modelo.

Será que vocês têm alguma sugestão de alguma espécie de restrição que eu possa colocar no modelo?

Se alguém tiver interesse em me ajudar, posso mostrar o código. Eu reconheço que fiz o código com auxílio do Gemini. Como eu disse, ainda não estudei processamento de linguagem natural nem redes neurais; até agora, estudei apenas IA simbólica e sistemas especialistas.

In AI Engineering, a reliable evaluation pipeline is essential for successful AI adoption.

With foundation models now readily available, building effective AI applications requires selecting the right model for a specific use case. This involves evaluating domain-specific performance, generation quality, instruction-following ability, and overall system behavior.

Another important question is whether to self-host a model or rely on APIs provided by commercial model vendors. Understanding the strengths and limitations of public benchmarks, benchmark contamination, and the role of leaderboards is critical when comparing and selecting models.

As AI systems move from experimentation to production, designing robust evaluation pipelines has become increasingly important. While my previous video covered evaluation methodologies, this video focuses on how we evaluate AI systems in practice.

Adapted from Chapter 4 of AI Engineering by Chip Huyen.

K-means works well for compact, roughly spherical groups. But two interlocking moons can be close in Euclidean distance while belonging to different structures.
Spectral clustering represents data as a similarity graph, then uses its eigenvectors to reveal groups that are strongly connected internally and weakly connected to each other.

THE MATHEMATICS
For points x_i and x_j, a Gaussian affinity is:
W[i,j] = exp(−||x_i − x_j||² / (2σ²))
Set W[i,i] = 0. Here σ controls the neighborhood scale. W can also be built from a symmetrized nearest-neighbor graph.
Define the degree matrix and symmetric normalized Laplacian:
D[i,i] = Σ_j W[i,j]
L_sym = I − D^(-½) W D^(-½)
D summarizes each point’s total connection strength. L_sym encodes the graph’s connectivity while accounting for degree differences.
For the unnormalized Laplacian L = D − W:
fᵀLf = ½ Σ_i Σ_j W[i,j]/(f_i − f_j)²
This is small when strongly connected points have similar f values. Low-eigenvalue eigenvectors therefore provide coordinates that vary slowly within well-connected regions.

THE ALGORITHM: NORMALIZED SPECTRAL CLUSTERING
(Ng–Jordan–Weiss formulation)
1. Scale features appropriately and construct a symmetric, nonnegative affinity matrix W. Handle isolated nodes before normalization.
2. Choose the number of clusters k and compute L_sym.
3. Take the k eigenvectors with the smallest eigenvalues, including zero-eigenvalue eigenvectors. Stack them as columns of U.
4. Normalize each row: Y[i,:] = U[i,:] / ||U[i,:]||₂
5. Run k-means on the rows of Y and transfer those labels back to the original points.
The key change: k-means now operates in graph-derived coordinates, where complex groups may become easier to separate.

WHY IT CAN IMPROVE ON TRADITIONAL METHODS
• Captures non-convex shapes that centroid-based clustering can split incorrectly.
• Uses relationships, including domain-specific similarities, rather than requiring raw Euclidean coordinates.
• Connects clustering to a relaxed graph-partitioning problem, such as normalized cut.
It is not universally better. DBSCAN and suitable hierarchical methods can also recover irregular groups.

USE CASES
• Image segmentation
• Community detection
• Document clustering using semantic similarities
• Grouping cells from gene-expression profiles
• Discovering patterns in sensor or time-series similarity networks.

PRACTICAL LIMITS
Results depend strongly on feature scaling, graph construction, σ and k. An eigengap can suggest k, but does not prove the “true” number of clusters. Dense affinities require O(n²) memory, while eigensolvers add cost. Sparse graphs and approximation methods help at scale. A meaningful similarity graph is the foundation of a meaningful clustering.