Stars
This repository contains machine learning pdf books
The lastest paper about detection of LLM-generated text and code
Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step
List of references and online resources related to data science, machine learning and deep learning.
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Jupyter notebooks for the Natural Language Processing with Transformers book
Collection of Summer 2025 tech internships!
2025 SWE internship & new graduate job list updated daily
👋 Hey there new grad🎉! We've put together a collection of full-time job openings for SWE, Quant, PM and tech roles in 2024! 🚀
BrainSurfCNN for individualized prediction of task contrasts from resting-state functional connectivity
sabunculab / text2brain
Forked from ngohgia/text2brainGenerating brain activation maps from free-form text query
Generating brain activation maps from free-form text query
List of companies offering Machine learning and Data Science internships
Simple A3C implementation with pytorch + multiprocessing
Codebase for CSC-591- Automated Software Engineering course @ NCSU (Group1)
📚 Freely available programming books
Tools to Design or Visualize Architecture of Neural Network
Deep Learning with TensorFlow and Keras – 3rd edition, Published by Packt
A dissertation/thesis template in LaTeX approval by the UGA Grad School.
🌳 Tiny & elegant JavaScript HTTP client based on the Fetch API
Collection of Summer 2024 Internships
A few starter examples of ansible playbooks, to show features and how they work together. See https://galaxy.ansible.com for example roles from the Ansible community for deploying many popular appli…
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
👩🏿🎓👨🏽🎓👩🏻🎓CNCF Mentoring: LFX Mentorship + Summer of Code
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Lab Materials for MIT 6.S191: Introduction to Deep Learning