TranslatedClose, paragraph-by-paragraph readings of classic and new deep learning papers
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- paper
- reading-list
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TranslatedClose, paragraph-by-paragraph readings of classic and new deep learning papers
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
PyTorch Tutorial for Deep Learning Researchers
An evolving how-to guide for securing a Linux server.
Roadmap to becoming an Artificial Intelligence Expert in 2022
This repository started out as a learning in public project for myself and has now become a structured learning map for many in the community. We have 3 years under our belt covering all things DevOps, including Principles, Processes, Tooling and Use Cases surrounding this vast topic.
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
A playbook for systematically maximizing the performance of deep learning models.
:memo: An awesome Data Science repository to learn and apply for real world problems.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
A one stop repository for generative AI research updates, interview resources, notebooks and much more!
This repository is maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), AI security, vulnerability research, exploit development, reverse engineering, and more. 🔥 Also check: https://hackertraining.org
Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
Machine Learning Systems: Foundations, Scaling, Agentic AI, and Physical AI (Vols I–IV) • Harvard CS249r | https://mlsysbook.ai
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
A curated list of awesome Deep Learning tutorials, projects and communities.
A complete daily plan for studying to become a machine learning engineer.
Extensive Open-Source Guides for Android Developers
Learn Low Level Design (LLD) and prepare for interviews using free resources.
30 Days of React challenge is a step by step guide to learn React in 30 days. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
deep learning for image processing including classification and object-detection etc.
The most cited deep learning papers