A4 thread blockingisevil

그림이 정상 출력되는 다음 링크의 자료를 확인해 주세요. https://www.slideshare.net/deview/233-network-load-balancing-maglev-hashing-scheduler-in-ipvs-linux-kernel

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

[224]네이버 검색과 개인화

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

The document discusses running a TensorFlow Serving (TFS) container using Docker. It shows commands to: 1. Pull the TFS Docker image from a repository 2. Define a script to configure and run the TFS container, specifying the model path, name, and port mapping 3. Run the script to start the TFS container exposing port 13377

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.

This document describes the steps to convert a TensorFlow model to a TensorRT engine for inference. It includes steps to parse the model, optimize it, generate a runtime engine, serialize and deserialize the engine, as well as perform inference using the engine. It also provides code snippets for a PReLU plugin implementation in C++.

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

[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터

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.

Clova 화자인식

This document describes the steps to convert a TensorFlow model to a TensorRT engine for inference. It includes steps to parse the model, optimize it, generate a runtime engine, serialize and deserialize the engine, as well as perform inference using the engine. It also provides code snippets for a PReLU plugin implementation in C++.

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

The document discusses various machine learning clustering algorithms like K-means clustering, DBSCAN, and EM clustering. It also discusses neural network architectures like bidirectional LSTMs, attention mechanisms, and convolutional sequence-to-sequence models. Finally, it shows charts comparing the performance of different chatbot models on validation data.

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.

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.