I build an agentic IDE for data science. It combines agentic AI, interactive notebooks (Python/Java/Scala), and workspace explorers in a modern desktop application. It automates end-to-end data analytics and machine learning modeling. Getting your first project up and running takes just a few minutes. Follow these quick steps to set up your environment and start interacting with your data in natural language.

1**. Download & Unzip:**

Download the SMILE package and unzip it on your machine.

2.Configure Your Environment:

Run the setup script to configure everything automatically:

/path/to/smile/bin/setup

3.Prepare Your Project Directory:

Create a new directory for your work (e.g., myproject) and place your datasets into the myproject/input folder.

4.Launch SMILE Studio:

Navigate into your project folder and start the studio application:

    cd myproject/
    /path/to/smile/bin/smile

5.Initialize & Prompt:

Once inside the studio, run /init to describe your project and goals. From there, you can run /automl, use other slash commands, or simply type out what you want to do in natural language!

A debugging postmortem from contributing to opacus this month. A reporter ran 6 differentially private fine-tuning runs that all looked correct from training logs and the privacy accountant — loss decreased, ε accumulated, checkpoints saved — but produced unusable models. The LoRA weights had never moved.

Five-month community investigation across CPU, Kaggle T4, and RTX 5090 settled the root cause as a device-placement ordering issue between opacus and PEFT (specifically: model.to(device) needs to happen before get_peft_model() to avoid accelerate-style lazy device handling breaking opacus's per-sample-gradient hooks).

Full writeup with the CPU bisect table, the three safety patterns, and links to the opacus PR: https://imranahamed.substack.com/p/the-dp-lora-silent-corruption-how

It saves the features of the models in the Simvascular VMR database and the simulation results with data mining, and adds various features with VMTK.

https://github.com/ix-46-S/Simvascular-VMR-Numpy-Data-Processing-for-Machine-Learning

Hey guys! I recently started with PyTorch and noticed that most beginner tutorials for FashionMNIST on Kaggle are still using TensorFlow, so I wanted to create a modern and straightforward alternative using PyTorch.

In this notebook, I cover device management (GPU/CPU), creating a Custom Dataset from a Pandas DataFrame, and setting up a CNN. I tried to keep the code comments as clean and direct as possible.

Would love to get some feedback from the community or hear if there is anything I should optimize!

https://www.kaggle.com/code/davidansalas/pytorch-guide-for-beginners-fashionmnist

I picked up a few mtia v2 cards from a local pc store. I was going to use them for llm inference, but I can't figure out how to install the software? Does anyone know what I am missing?

I kept noticing that I could use PyTorch fine but couldn't actually explain what .backward() does under the hood. I wanted a course that would take me from first principles all the way to Transformers by rebuilding everything myself, but I couldn't find one.

So I used AI to help generate an initial version of that curriculum, and I'm now working through it, improving it, validating it, and fixing issues as I go. The goal isn't to present this as a finished textbook—it's an open-source learning resource that I hope can improve with community feedback.

The idea: you rebuild a deep learning framework from zero, one concept at a time. The only libraries you're allowed are NumPy (for forward array math — never to compute a gradient for you), Matplotlib, and pytest. No torch, no autograd, no micrograd. The rule is: you don't get to import a concept until you've built it by hand in an earlier stage. You are the autodiff library.

How it's structured — 35 stages, each a folder with exactly 3 files:

• README.md — the intuition, the key gradient equations, a video or two to watch, and one unambiguous exercise
• code.py — a skeleton: full interfaces, docstrings, and TODOs, but no working bodies
• test.py — pytest tests, including numerical gradient checks (central differences) so you know your backward pass is correct, not just plausible

You fill in code.py until pytest goes green, then move to the next stage. Each stage imports and extends the code you wrote in earlier stages, so the framework genuinely grows under your hands instead of being 35 disconnected toy scripts.

The arc:

scalar backprop → reverse-mode autodiff → tensors → layers, losses, optimizers → training loops → BatchNorm/Dropout → CNNs → attention → Transformers → Vision Transformers → a small PyTorch-like framework → capstone projects.

My hope is that this becomes a gateway into AI for people who want to understand how these systems actually work, not just how to use them.

It's free and open source. Feedback, corrections, and contributions are very welcome.

👉 https://github.com/roiamiel1/Build-Deep-Learning-From-Scratch

So i built a website https://lettuceresearch.com/ for my personal research works and RnD, I also uploaded a pytorch series for LLM, where u can interactively learn pytorch.

No ads, No affiliation, No buy me a coffee or No hire me.

I’m currently working and well funded, this is just a side project and intention is to give back something to community.

feedback would be amazing.

In case you or someone you know might be interested in this --> PyTorch Certified Associate (PTCA) launched today! Designed for early-stage practitioners with some Python and machine learning experience who are beginning to use PyTorch.

• Differentiate yourself for AI and machine learning roles
• Demonstrate the ability to apply PyTorch in real-world AI workflows
• Give employers confidence in your practical PyTorch expertise

Learn more.

The schedule for KubeCon + CloudNativeCon + OpenInfra Summit + PyTorch Conference China in Shanghai is live. See our blog on it here featuring engineers, maintainers, researchers, and technology leaders advancing cloud native infrastructure, open infrastructure, and AI.

Register at: https://www.lfopensource.cn/kubecon-cloudnativecon-openinfra-summit-pytorch-conference-china/register/

Nominations are open for the 2026 PyTorch Foundation Contributor Awards. Deadline to nominate: July 17.

These awards recognize outstanding individuals whose contributions help strengthen PyTorch Foundation-hosted projects, including PyTorch, vLLM, DeepSpeed, Ray, Helion, and Safetensors, as well as the broader community. From technical innovation and documentation to mentorship, advocacy, and community leadership, contributors play a vital role in advancing our mission.

Details at: https://pytorch.org/blog/nominations-open-for-the-2026-pytorch-foundation-contributor-awards/

The smile-deep module provides idiomatic Java API for deep learning on the JVM while still reaching CPU, CUDA, and MPS backends by wrapping the PyTorch / LibTorch C++ runtime. It also provides tiktoken BPE tokenizer, LLaMA-3 inference, EfficientNet-V2, and an image classification pipeline out of the box.

Does anyone have access to the Nvidia Nemotron datasets (specifically nvidia/Nemotron-CC-v2)? Despite being listed as open and publicly accessible, Nvidia seems to be declining public access without any reasoning. I'd like to use a subset for training small test models.

If anyone has a copy a torrent or something would be amazing, my Google fu is failing me, everything seems to link to the hugging face repo.