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How feature usage informs our understanding of overfitting in deep networks Photo by Shane Aldendorff on Unsplash Overfitting is a central problem in machine learning that is strongly tied to the reliability of a learned model when it is deployed on unseen data. Overfitting is often measured — or even defined — by the difference in accuracy obtaine ...
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Diagram of MURAL, our method for learning uncertainty-aware rewards for RL. After the user provides a few examples of desired outcomes, MURAL automatically infers a reward function that takes into account these examples and the agent’s uncertainty for each state. Although reinforcement learning has shown success in domains such as robotics, chip pl
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How to select the right feature store for your use case Image by Evgeny Tchebotarev via Pexels Wading through the options of any tool in a burgeoning SaaS vertical reminds me of that song from The Wizard of Oz — Over the Rainbow — originally sung by Judy Garland yet covered to perfection by Hawaiian activist Israel Kamakawiwoʻole. Somewhere over th ...
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arXiv DOOM is a parody of the ever-increasing number of papers that appear on arXiv every day, it allows you to fight the one hundred most-recent papers in the cs.CV category.
Play here: https://sniklaus.com/arxivdoom
Watch demo: https://twitter.com/simon_niklaus/status/1450863810160459777 ...
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mlforecast makes forecasting with machine learning fast & easy By Nixtla Team . TL;DR: We introduce mlforecast, an open source framework from Nixtla that makes the use of machine learning models in time series forecasting tasks fast and easy. It allows you to focus on the model and features instead of implementation details. With mlforecast you can ...
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MLOps without too much Ops — Episode 2 With Andrea Polonioli and Jacopo Tagliabue Photo by Stephanie LeBlanc via Unsplash While the number of Machine Learning (ML) applications used in production is growing, not a day goes by when we don’t read something about how most enterprises still struggle to see positive ROI (see here and here). One thing yo ...
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Assemble arbitrary real-life puzzles using basic AI tools Image by author (made with “Life under the Arctic ice” by Castorland Puzzle) Intro Since the early days of AI, we’ve seen multiple attempts to handle a jigsaw puzzle problem. However, enthusiasts mainly focus on specific aspects: only square-tile, only non-scanned, monochrome, etc. Here we t ...
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Hey guys, so I’m a CS DS double major with a minor in stats. I am tailoring my degrees to ML and learning ML on the side as well. Has anyone had luck entering the field with just a bachelors? Everywhere I see and read says we need a masters at a minimum. Does anyone want to share their experience / input / advice? Thank you! ...
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Hi everyone. Not sure how interested /r/MachineLearning is in crypto, but figured that you were the perfect folks to present this dataset: https://www.kaggle.com/simiotic/ethereum-nfts
Our dataset contains 7,020,850 NFT transactions that took plac ...
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Machine learning algorithms for anomaly detection can assist DevOps in daily working routines, where generalized ML models are trained and applied to detect hidden patterns and identify suspicious behaviour. Applied machine learning for IT-operations (AIOPs) is starting to move from research environments to production environments in companies. By
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The majority of medical AI solutions crash and burn in the real world. It turns out that accuracy isn’t enough. A human-centred design approach is critical to the success of AI in healthcare, as Google also found out. Image by macrovector — www.freepik.com “What healthcare needs is human-centered AI: artificial intelligence that is not merely drive ...
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I recently did a take home assignment and was told that there were major mistakes in it, not in terms of DS concepts but for the given assignment.
The assignment was to predict daily sales for multiple stores. The data had sales, timestamp and store level information about promocodes, assortment etc and had missing data.
At first it does loo ...
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The TorchVision datasets subpackage is a convenient utility for accessing well-known public image and video datasets. You can use these tools to start training new computer vision models very quickly. TorchVision Datasets Example To get started, all you have to do is import one of the Dataset classes. Then, instantiate it and access one of the samp
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Posted by Alisa Chang, Software Engineer, Google Cloud and Pritish Kamath, Research Scientist, Google Research Over the last several years, progress has been made on privacy-safe approaches for handling sensitive data, for example, while discovering insights into human mobility and through use of federated analytics such as RAPPOR. In 2019, we rele ...
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Dear All,
The PDF of my book "Deep Learning Interviews", can now be downloaded from:
https://github.com/BoltzmannEntropy/interviews.ai
(direct link:
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