www.bing.com/ck/a?!&&p=d796d45d250e9ef3a124ba32fa239e9ae08a5d20012fcfbca631ecb1c49b1229JmltdHM9MTc3Mjc1NTIwMA&ptn=3&ver=2&hsh=4&fclid=24630743-8151-6ca1-3e77-105680b76d53&u=a1aHR0cHM6Ly93d3cuZWxldmVuZm9ydW0uY29tL3QvbmV3LW1hY2hpbmUtYXVkaW8tcG9wcy1mb3VuZC1vdXQtYWJvdXQtbGF0ZW5jeW1vbi1yZXN1bHQtc2VlbXMtYmFkLWFueS1hZHZpY2UtYXBwcmVjaWF0ZWQuMTUxNDIv&ntb=1
May 25, 2023 · New machine. WIN 11 PRO. Cpu passes prime 95. memory passes memtest 86, gpu passes furmark for 30 mins, machine overall hits average for specs on timespy with 0 overclocking. …
arxiv.org/abs/1805.12282v1
We examine how various types of noise in the parallel training data impact the quality of neural machine translation systems. We create five types of artificial noise and analyze how they degrade performance in neural and statistical machine translat...
arxiv.org/abs/2304.01523v1
Context: On top of the inherent challenges startup software companies face applying proper software engineering practices, the non-deterministic nature of machine learning techniques makes it even more difficult for machine learning (ML) startups....
github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
500 AI Machine learning Deep learning Computer vision NLP Projects with code (⭐ 32078)
github.com/faizelkhan/Insurance-Claim-Using-Machine-Learning-
Predicting property and casualty insurance claims: A Machine Learning Approach (⭐ 6)
github.com/Building-ML-Pipelines/building-machine-learning-pipelines
Code repository for the O'Reilly publication "Building Machine Learning Pipelines" by Hannes Hapke & Catherine Nelson (⭐ 609)
github.com/ashishpatel26/Last-Minute-Notes-of-Machine-learning-and-Deep-learning
Last Minute Note of Machine learning and Deep learning by Jason Brownlee (⭐ 92)
github.com/susanli2016/Machine-Learning-with-Python
Python code for common Machine Learning Algorithms (⭐ 4562)
github.com/googlecreativelab/aiexperiments-drum-machine
Thousands of everyday sounds, organized using machine learning. (⭐ 760)
arxiv.org/abs/2509.10590v2
Machine Unlearning (MU) has emerged as a promising approach to addressing persistent challenges in Machine Learning (ML) systems. By enabling the selective removal of learned data, MU introduces protective, corrective, and adaptive capabilities that...
github.com/rajyash1904/ATM-Machine
This Project is to make an automated Teller machine with user's Account Number, and Password, bank account, with that users can withdraw, deposit, and view their account balance. (⭐ 242)
arxiv.org/abs/2311.03767v1
Neural Machine Translation (NMT) models are state-of-the-art for machine translation. However, these models are known to have various social biases, especially gender bias. Most of the work on evaluating gender bias in NMT has focused primarily on En...
github.com/rasbt/python-machine-learning-book-3rd-edition
The "Python Machine Learning (3rd edition)" book code repository (⭐ 4998)
github.com/rasbt/python-machine-learning-book-2nd-edition
The "Python Machine Learning (2nd edition)" book code repository and info resource (⭐ 7205)
github.com/rasbt/machine-learning-book
Code Repository for Machine Learning with PyTorch and Scikit-Learn (⭐ 5028)
github.com/rasbt/python-machine-learning-book
The "Python Machine Learning (1st edition)" book code repository and info resource (⭐ 12595)
github.com/WongYatChun/Advances-in-Financial-Machine-Learning
Notes on Advances in Financial Machine Learning (⭐ 84)
arxiv.org/abs/1508.03843v1
Gremlin is a graph traversal machine and language designed, developed, and distributed by the Apache TinkerPop project. Gremlin, as a graph traversal machine, is composed of three interacting components: a graph $G$, a traversal $Ψ$, and a set of tr...
arxiv.org/abs/1611.08309v1
We study the problem of troubleshooting machine learning systems that rely on analytical pipelines of distinct components. Understanding and fixing errors that arise in such integrative systems is difficult as failures can occur at multiple points in...
github.com/rasbt/stat479-machine-learning-fs19
Course material for STAT 479: Machine Learning (FS 2019) taught by Sebastian Raschka at University Wisconsin-Madison (⭐ 780)
