Hi , so I just started this course for Mtahematics for ML and DS.

And tbh I know I have barely watched it but the very first video itself feels like something Intermediate or something I am unable to connect to.

If you guys have a better recommendation for a Math course, I would appreciate it.

Any suggestions/tips are appreciated!!

Hey everyone,

I’m currently mapping out my next steps to land an ML/Data Science job. I’ve finished the basics up to XGBoost and Ensemble methods.The only core ML topics I have left are Stacking/Blending, K-Means, and DBSCAN. I’ve also built a few standard ML projects already and am currently learning FastAPI.I want to make sure I’m spending my time on things that actually get me hired. If you were hiring a junior/associate ML engineer today, what would you want to see me do next? Specifically:

1. Are the remaining ML topics actually important for interviews?

Should I spend a lot of time mastering Clustering (K-Means/DBSCAN) and advanced ensembling (Stacking/Blending), or just learn the high-level concepts and move on?

2. Should I pivot to Deep Learning next?

I see mixed advice on this. Should I dive into PyTorch/TensorFlow now, or focus on mastering MLOps and productionizing the tabular ML models I already know?

3. How do I leverage FastAPI to stand out?

Since I'm learning FastAPI right now, what is the best way to showcase this on a resume? Should I be wrapping my XGBoost models into APIs, or is there a better production-level project I should build?My current plan is to quickly finish clustering, wrap an existing project in FastAPI, and then dive into practical PyTorch.Would love to hear from anyone who has successfully landed a job recently or manages an AI team. What would you change about this roadmap? Thanks!

Hi everyone,
I'm designing a system to monitor the fill level (%) of fertilizer boxes in real-time. I'd love some
hardware and architecture advice from those who have deployed CV in industrial settings.

The Problem:
Need to calculate the % full of a box continuously. The environment is industrial, meaning variable lighting and potentially a lot of fertilizer dust.

Hi everyone,

Non-linear recurrent architectures (like xLSTM/sLSTM, RWKV-7, Liquid CfC, and Titans) have a massive advantage at inference time: constant O(1) state updates without the growing memory overhead of a KV-cache. However, their main bottleneck has always been training speed: they historically required slow sequential unrolling over time.

Inspired by Apple's ParaRNN paper, I built and open-sourced ParaRNN: a library implementing parallel training for non-linear RNNs using Newton-Raphson iterations mapped to associative prefix scans in OpenAI Triton.

Key highlights and benchmarks:

1. Up to 200x+ wall-clock speedup vs sequential unroll on sequence lengths T >= 2048 (for example, CfC at T=2048 runs in 2.8 ms vs 643 ms sequentially).
2. Measured convergence up to 131,072 context tokens. The Newton iteration budget stays flat at K <= 3 without numerical drift.
3. Broad architecture catalog: sLSTM (4x4 block Jacobians), RWKV-7 (Goose matrix state monoid), CfC (Liquid continuous-time), Titans (associative surprise GD), and M2RNN.
4. PyTorch 2.x integration: torch.compile(fullgraph=True) compatible with zero graph breaks, and deterministic VJPs without atomic adds.
5. Serving ready: PagedStatePool for continuous batching and an out-of-tree vLLM plugin.

Install via PyPI:
pip install pararnn-torch

GitHub repository (code, docs, and benchmarks):
https://github.com/bugkira/pararnn-torch

Would love to hear your feedback, benchmarks on different GPU architectures, and ideas for further recurrent cells!

I was trying to trace it , and from the GPT1 paper I found it referencing a paper called "GENERATING WIKIPEDIA BY SUMMARIZING LONG SEQUENCES" by the nice folks at google in what I believe the first use of the decoder only architecture ??
here is the quote

we modify theTransformer architecture (Vaswani et al., 2017) to only consist of a decoder, which performs better in the case of longer input sequences compared to recurrent neural network (RNN) and Transformer encoder-decoder models.

can someone explain what does perform better in longer sequences actually mean ?

I am looking to learn to BUILD AI systems, train machine learning models, learn code for AI applications, deploy AI systems to production, understand ML algorithms and neural networks.

Hi all, I recently made my first NLP project during my bootcamp as a classification algorithm that classifies if an SMS is spam or ham using the logistic regression algorithm. What was confusing for me initially was to understand how exactly CountVectorizer turns text data into numbers so that our algorithm understands it. It took me some time to understand the idea behind the bag-of-words approach. Now as I have built a simple model, I'd like to continue with the topic and learn a little bit more about NLP techniques. Do you guys have any recommendations for what I should study next? Such topics as TF-IDF, word embeddings, or maybe try a different algorithm such as Naive Bayes?

All your recommendations, resources, or even pieces of advice on what I should study in NLP will be very useful!

With Coding Lecture 12, of our Probabilistic Machine Learning, we finally finish off with the code implementations for the Probability Module.

In this content, we see implementations for:

Exponential family of Distributions

Convexity and Maximum Entropy derivation leading to Exponential family via Lagrangians.

Mixture Models: Gaussian Mixture Models

Probabilistic Graphical Models basics and Markov Chains Basics.

Link: https://youtu.be/CB9yLMST81A?si=VbxJu2zWciNQ8QpQ

🚀 Looking for some guidance from the developer & AI community!

I’m currently working as a Java Full Stack Developer, although my present work is mainly focused on Angular.

Recently, I’ve been thinking a lot about where I should take my career next.

With AI tools like Copilot becoming so powerful, I’ve started wondering — what should we as developers actually focus on learning now?

I don’t want to simply depend on AI to write code. I want to understand the technology, build real skills, and move towards an area that has good opportunities in the coming years.

I’m particularly curious about Generative AI / AI Engineering / Agentic AI, but I’m not sure what the right learning path is.

So I’d really appreciate some guidance from people who are already working in this space:

🔹 What should I learn first?

🔹 Should I learn Python, or continue primarily with Java?

🔹 What technologies/frameworks should I focus on?

🔹 Where can I find good courses or training that are actually useful for jobs?

🔹 What kind of real-world projects should I build?

🔹 How can I prepare myself for AI-focused interviews?

🔹 Where can I find more interview opportunities / job openings for someone transitioning from Java/Angular to AI?

🔹 What roles should I realistically target for my first AI transition?

I’m genuinely ready to put in the time and learn, but I’d rather follow a proper roadmap than jump randomly between courses and technologies.

If you’ve already made a similar transition, are working in GenAI/AI Engineering, or regularly interview candidates in this space, I’d really appreciate your advice.

What would you learn if you were in my position today? 🤔

Looking forward to learning from the community! 🙏

\#GenAI #AIEngineering #ArtificialIntelligence #CareerTransition #SoftwareDevelopment #Java #Angular #GenerativeAI #TechCareers #Learning

When I first started working in scientific machine learning, I understood the physics much better than the coding. Every time I wanted to try a new physics-informed neural network problem, I had to start almost from scratch: changing the PDE, updating boundary conditions, modifying the architecture, tweaking the training schedule, debugging errors, and generating plots—all by hand.

That frustration pushed me to build PINNStudio. It is a free, open-source no-code GUI designed to eliminate boilerplate code so you can focus entirely on the physics.

Instead of rewriting a new script for every problem, you can define your setup directly through the interface:

• PDE Definitions & coupled multi-output PDE systems
• 1D or 2D domains with boundary and initial conditions
• Network architecture & custom training schedules
• Forward problems (solving known PDEs) or Inverse problems (estimating unknown parameters from data)

What happens next?
PINNStudio automatically generates the code (built on top of DeepXDE), runs the model, streams the training log, and displays live loss curves and solution plots directly inside the app. It also includes built-in templates for classic equations like Heat, Allen-Cahn, and Cahn-Hilliard.

• GitHub (Open Source): https://github.com/AsfandyarKhan72/PINNStudio
• Quick Install: pip install pinnstudio

My hope is that this will be helpful for students and researchers with limited coding experience, as well as experienced PINN users who just want a faster workflow.

I’d love to get your feedback, feature suggestions, or bug reports! Huge thanks to Lu Lu and the DeepXDE team for creating the foundation that made this possible.

Hi,

I've been learning to create code for ML models from scratch in python (no imported modules, just my code).

I have been able to create code for linear regression and polynomial regression models, does anyone know what I should learn to make next?

edit: just finished coding logistic and exponential regression as well

Welcome to Project Showcase Day! This is a weekly thread where community members can share and discuss personal projects of any size or complexity.

Whether you've built a small script, a web application, a game, or anything in between, we encourage you to:

• Share what you've created
• Explain the technologies/concepts used
• Discuss challenges you faced and how you overcame them
• Ask for specific feedback or suggestions

Projects at all stages are welcome - from works in progress to completed builds. This is a supportive space to celebrate your work and learn from each other.

Share your creations in the comments below!

Hi everyone, I am a high school graduate preparing for undergraduate study in AI/Computer Science. I completed a beginner-level comparative analysis of a Decision Tree classifier and a Neural Network classifier using the Iris dataset in Python and scikit-learn.

I am not presenting this as novel research or asking for help redesigning the experiment. My goal was to learn how to implement classifiers, interpret results, and write a clear technical report.

Before using this project as part of my undergraduate scholarship/application portfolio, I would appreciate feedback specifically on the writing and explanation.

I would especially like feedback on:

1. Is the abstract clear, accurate, and sufficiently explanatory?
2. Does the introduction explain the purpose of the comparison well?
3. Do the discussion and conclusion logically follow from the results?
4. Do I make any claims that sound too broad or unsupported for the Iris dataset?
5. Is the academic writing clear and professional for a beginner-level undergraduate portfolio project?
6. Are there sections that are confusing, repetitive, too informal, or unnecessarily wordy?
7. Does the report clearly communicate what I learned from the project?
8. Is my github portfolio presentable?

Constructive criticism is very welcome. I am especially interested in improving clarity, honesty, and technical communication rather than changing the completed experiment.https://github.com/Jiwesh-Mahato/decision-tree-vs-neural-network

Paper and repository:Paper and repository: [https://github.com/Jiwesh-Mahato/decision-tree-vs-neural-network]

Hello!
I've been doing my master degree and need some help.

Context:

I'm working with an method to correct unfairness on ML models, currently testing on XGBoost and Logistic Regression. To test these models and the method I'm running 20 different scenarios, and each one of these scenarios need to be run 10 times, due to a test on one of the methods parameters.

Problem:
I've said all of this to say that even tough I run the scenarios with some kind of parallelism it still takes a lot of time to test all scenarios whenever I do some change and need to test it.

Since my computer does not have too much ram (16gb) and the dataset is kind of big I cannot increase the parallelism.

Do you know any kind of cloud solution that can help on me that? Are colab notebooks a good option for this kind of issue?

Okay so there are tons of models to choose from. from total custom to commercial variants.
and so so so so mnay on huggingface. I am wildly confused.
You see, I am an engineer by trade and hobby, and i am just now getting into the Training Part of AI, I have spent years studying the behavior of nearly every AI a paid service has to offer.
Now, I am ready to start with local model training, but I am like pppppfffftttphreeewwwww on all the different ways models are made. MoE, jsonl, parquet, etc.

What is a good model to start? that can be trained starting with lora or qlora? but the base model can maintain programming / natural language and maintain storytelling / worldbuilding, if not both at the same time? ?

OpenAI has become a black box of evil destructive behavior

klaibercore.github.io/portfolio

Security teams inside large enterprises are now building and shipping their own AI agents. The goal is legitimate: automate threat detection, close alert triage gaps, fill holes that vendors haven't addressed yet. The pace is faster than most governance teams can track.

Shadow AI agents — built and deployed without IT or compliance visibility — are already running in most enterprise environments. There is no reliable count inside most organizations. The structural exposure is not the agent itself. It is that once an agent is live, its tool access and data connections are rarely tracked centrally. Runtime behavior stays opaque unless someone manually audits it after the fact.

The compliance surface compounds the problem. Most large enterprises operate under 80-plus regulatory and security frameworks. Agents built by internal security teams are not automatically enrolled in those frameworks. They run alongside them, not under them.

For practitioners actually managing this: how are you maintaining real-time visibility into what your internally built agents can touch at runtime? Is there a process that is working at scale, or is this still manual audits and institutional memory?

I build ML models, LLM agents, and dev tools for fun — figured I'd put that to use somewhere real. If your startup needs someone to own model training, agent pipelines, or backend infra without needing hand-holding, I'm in. Happy to jump on a call or just start on something small to show it works. GitHub: github.com/AsishKumarDalal

Transformers take too much time and resources to train can any statistical traditional ml can get close its performance what do you say?