rajat5ranjan/Machine-Hack
MachineHack is an online platform for Machine Learning competitions. We host toughest business problems that can now find solutions in Machine Learning & Data Science. (⭐ 19)
MachineHack is an online platform for Machine Learning competitions. We host toughest business problems that can now find solutions in Machine Learning & Data Science. (⭐ 19)
An implementation of a complete machine learning solution in Python on a real-world dataset. This project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem! (⭐ 1257)
Official repository for CMU Machine Learning Department's 10721: "Philosophical Foundations of Machine Intelligence". (⭐ 262)
A mind machine (aka brain machine or light and sound machine) uses pulsing rhythmic sound, flashing light, or a combination of these. Mind machines can
A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine…
Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrat…
Machine teaching is an inverse problem of machine learning that aims at steering the student learner towards its target hypothesis, in which the teacher has already known the student's learning parameters. Previous studies on machine teaching focused...
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Start here if... You're new to data science and machine learning, or looking for a simple intro to the Kaggle prediction competitions. Competition Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912,…
With the Increasing use of Machine Learning in Android applications, more research and efforts are being put into developing better-performing machine learning algorithms with a vast amount of data. Along with machine learning for mobile phones, the...
The deaf-mute community have undeniable communication problems in their daily life. Recent developments in artificial intelligence tear down this communication barrier. The main purpose of this paper is to demonstrate a methodology that simplified Sign Languag…
Recent advancements in Neural Machine Translation (NMT) models have proved to produce a state of the art results on machine translation for low resource Indian languages. This paper describes the neural machine translation systems for the English-Hin...
Machine Learning Foundations is a free training course, from Google, where I’ll learn the fundamentals of building machine learned models using TensorFlow. (⭐ 1 | Python)
Azure Machine Learning prompt flow is a development tool designed to streamline the entire development cycle of AI applications powered by Large Language Models (LLMs).
Train and deploy machine learning models with Azure Machine Learning. Get started with quickstarts, explore tutorials, and manage your ML lifecycle with MLOps best practices.
Find out how to deploy a new version of a machine learning model without disruption. See how to use a blue-green deployment strategy in Azure Machine Learning.
Azure Machine Learning prompt flow is a development tool designed to streamline the entire development cycle of AI applications powered by Large Language Models (LLMs).
Create an Azure Machine Learning workspace and cloud resources that can be used to train machine learning models.
Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today’s most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artifi…
The behaviour of building occupants in the first stage of an evacuation can dramatically impact the time required to evacuate buildings. This behaviour has been widely investigated by scholars with a macroscopic approach fitting random distributions to represe…