[142] Learn how Material Design makes it easier and faster to build apps without compromising quality

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The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise boosts blood flow, releases endorphins, and promotes changes in the brain which help enhance one's emotional well-being and mental clarity.

Technology

[142] Learn how Material Design makes it easier and faster to build apps without compromising quality

The document discusses challenges with using reinforcement learning for robotics. While simulations allow fast training of agents, there is often a "reality gap" when transferring learning to real robots. Other approaches like imitation learning and self-supervised learning can be safer alternatives that don't require trial-and-error. To better apply reinforcement learning, robots may need model-based approaches that learn forward models of the world, as well as techniques like active localization that allow robots to gather targeted information through interactive perception. Closing the reality gap will require finding ways to better match simulations to reality or allow robots to learn from real-world experiences.

[243] Deep Learning to help student’s Deep Learning

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This document describes research on using deep learning to predict student performance in massive open online courses (MOOCs). It introduces GritNet, a model that takes raw student activity data as input and predicts outcomes like course graduation without feature engineering. GritNet outperforms baselines by more than 5% in predicting graduation. The document also describes how GritNet can be adapted in an unsupervised way to new courses using pseudo-labels, improving predictions in the first few weeks. Overall, GritNet is presented as the state-of-the-art for student prediction and can be transferred across courses without labels.

This document presents a formula for calculating the loss function J(θ) in machine learning models. The formula averages the negative log likelihood of the predicted probabilities being correct over all samples S, and includes a regularization term λ that penalizes predicted embeddings being dissimilar from actual embeddings. It also defines the cosine similarity term used in the regularization.

[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기

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[224]네이버 검색과 개인화

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[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)

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[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기

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[213] Fashion Visual Search

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The document discusses linear algebra concepts including: - Representing a system of linear equations as a matrix equation Ax = b where A is a coefficient matrix, x is a vector of unknowns, and b is a vector of constants. - Solving for the vector x that satisfies the matrix equation using linear algebra techniques such as row reduction. - Examples of matrix equations and their component vectors are shown.

[232] TensorRT를 활용한 딥러닝 Inference 최적화

NAVER D2

[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지

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[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터

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[223]기계독해 QA: 검색인가, NLP인가?

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The document discusses machine reading comprehension (MRC) techniques for question answering (QA) systems, comparing search-based and natural language processing (NLP)-based approaches. It covers key milestones in the development of extractive QA models using NLP, from early sentence-level models to current state-of-the-art techniques like cross-attention, self-attention, and transfer learning. It notes the speed and scalability benefits of combining search and reading methods for QA.

[231] Clova 화자인식

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[232]TensorRT를 활용한 딥러닝 Inference 최적화

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[222]누구나 만드는 내 목소리 합성기 (부제: 그게 정말 되나요?)

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Old Version: [211] 인공지능이 인공지능 챗봇을 만든다

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[241] AI 칩 개발에 사용되는 엔지니어링

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The document discusses designing hardware chips for artificial intelligence. It emphasizes that microarchitecture exploration is important and takes time, suggesting allocating 6-8 months to build performance models. The document also explains that hardware description languages like Verilog and VHDL are used to specify chip designs at the logic gate level, and that physical compilation translates this into an actual chip design. Overall, the document provides an overview of the chip design process from exploring microarchitectures to physical implementation.

[246]QANet: Towards Efficient and Human-Level Reading Comprehension on SQuAD

NAVER D2

Adams Wei Yu is a PhD candidate at CMU working on machine reading comprehension and large scale optimization. His advisors are Jaime Carbonell and Alex Smola. He has worked on question answering models and datasets like SQuAD. QANet is one of his contributions, which uses self-attention and convolutional layers instead of RNNs for question answering. It achieves state-of-the-art results while being much faster to train and run than previous models.

[221] 이미지를 이해하는 이미지검색: 텍스트기반 이미지검색에 CNN 이용하기

NAVER D2

[132] 서비스 오리엔티드 블록체인을 위한 스케일링 문제 해결

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Cosa hanno in comune un mattoncino Lego e la backdoor XZ?

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ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune. Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile. BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).

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Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.

Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf

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Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers: * What is Vector Search? * Importance and benefits of vector search * Practical use cases across various industries * Step-by-step implementation guide * Live demos with code snippets * Enhancing LLM capabilities with vector search * Best practices and optimization strategies Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications. #MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology

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How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024? In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores. See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe. This webinar will review: - The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey - The top challenges for privacy leaders, practitioners, and organizations in 2024 - Key themes to consider in developing and maintaining your privacy program

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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...

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