2021年が濃すぎて振り返りきれない｜松岡玲音
昨年初めてnoteで一年の振り返り記事を書いてみて、なんか良かったので今年も振り返りを書こう！と思ったのだが、2021年があまりに充実しすぎていて、振り返りきれない😇 ので、2021年に立てたOKRだけでもまずは振り返り、2022年の目標を立てることでお茶を濁していくことにする。 2021年の目標（OKR）の振り返りObjective 1: エンジニアとして継続的に成長し、事業に貢献する2020年は、メルカリUSという新たな環境で結果を出すためにがむしゃらに仕事をした年だった。数字に出るような成果を残すことはできたが、エンジニアとしてちゃんと成長できているのか？という課題感があったため、1つ目のObjectiveに、「継続的な成長」を盛り込んだ。 KR1: バックエンド、データエンジニアリング、情報検索についてそれぞれ課題図書を数冊決め、読み切る - 達成度: 60%「Webを支える技術
GitHub - NVIDIA-Merlin/Transformers4Rec: Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.
Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and can work with PyTorch. The library works as a bridge between natural language processing (NLP) and recommender systems (RecSys) by integrating with one of the most popular NLP frameworks, Hugging Face Transformers (HF). Transformers4Rec makes state-of-the-art transformer architectures available
Less… Is More? Apple’s Inconsistent Ellipsis Icons Inspire User Confusion - TidBITS
Image by PublicDomainPictures from Pixabay As I’ve been exploring iOS 13 to write the just-released Take Control of iOS 13 and iPadOS 13, I’ve become concerned about what seems to be an increasingly frequent pattern in iOS software design. What finally pushed me over the edge into writing this article was documenting Apple Card’s user interface in Wallet (see “How to Get the Most from Your Apple C
Transformers4Rec: A flexible library for Sequential and Session-based recommendation
Recommender systems help users to find relevant content, products, media and much more in online services. They also help such services to connect their long-tailed (unpopular) items to the right people, to keep their users engaged and increase conversion. Traditional recommendation algorithms, e.g. collaborative filtering, usually ignore the temporal dynamics and the sequence of interactions when
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