The document discusses building a real-time collaborative web tool using operational transformation (OT). OT allows multiple users to collaboratively edit a document by transforming and replicating document operations across clients. The basic concepts covered include the data model, operation model, and OT functions. OT works by having each client execute operations, transmit operations to other clients, and transform operations so that all clients remain in sync despite simultaneous edits. This replicated architecture and use of OT functions helps enable real-time shared editing of documents.
The document discusses building a real-time collaborative web tool using operational transformation (OT). OT allows multiple users to collaboratively edit a document by transforming and replicating document operations across clients. The basic concepts covered include the data model, operation model, and OT functions. OT works by having each client execute operations, transmit operations to other clients, and transform operations so that all clients remain in sync despite simultaneous edits. This replicated architecture and use of OT functions helps enable real-time shared editing of documents.
The document compares and contrasts several JavaScript testing frameworks for node.js applications including Nodeunit, Vows, Mocha, Jasmine-node, and BusterJS. It outlines the pros and cons of each framework, such as their syntax, support for asynchronous code, browser testing capabilities, and extensibility with other libraries. Additional tools mentioned include assertion libraries, spies, utilities for running client-side tests from the terminal, and links to documentation.
The document discusses how to use RxJS (Reactive Extensions library for JavaScript) to treat events like arrays by leveraging Observable types and operators. It explains key differences between Observables and Promises/Arrays, how Observables are lazy and cancelable unlike Promises. Various RxJS operators like map, filter, interval and fromEvent are demonstrated for transforming and composing Observable streams. The document aims to illustrate how RxJS enables treating events as collections that can be processed asynchronously over time.
- A component diagram shows the organization and dependencies among physical software components, including source code, runtime code, and executables. It addresses the static implementation view of a system and represents high-level reusable parts.
- The key elements are components, interfaces, ports, and connectors. Components provide and require interfaces. Interfaces can be attached to ports, which control component interactions. Connectors link components through ports or interfaces.
- A deployment diagram models the physical deployment of artifacts across nodes like hardware. It shows the configuration of runtime processing nodes and the artifacts deployed on them, such as executable files, libraries, and tables.
Sales & Operations Planning (S&OP): An IntroductionSteelwedge
ย
Do you know the secret to a successful Sales and Operations Planning process?
Your ability to troubleshoot issues, plan for unexpected events, and maintain a reliable, single set of planning numbers is drastically affected by people, process and technology.
Educate your colleagues or refresh your own skills with the new introduction to S&OP presentation.
For more information about S&OP and how Steelwedge can help your business, please visit: http://www.steelwedge.com/resources/sales-and-operations-planning-intro/
The document compares and contrasts several JavaScript testing frameworks for node.js applications including Nodeunit, Vows, Mocha, Jasmine-node, and BusterJS. It outlines the pros and cons of each framework, such as their syntax, support for asynchronous code, browser testing capabilities, and extensibility with other libraries. Additional tools mentioned include assertion libraries, spies, utilities for running client-side tests from the terminal, and links to documentation.
The document discusses how to use RxJS (Reactive Extensions library for JavaScript) to treat events like arrays by leveraging Observable types and operators. It explains key differences between Observables and Promises/Arrays, how Observables are lazy and cancelable unlike Promises. Various RxJS operators like map, filter, interval and fromEvent are demonstrated for transforming and composing Observable streams. The document aims to illustrate how RxJS enables treating events as collections that can be processed asynchronously over time.
- A component diagram shows the organization and dependencies among physical software components, including source code, runtime code, and executables. It addresses the static implementation view of a system and represents high-level reusable parts.
- The key elements are components, interfaces, ports, and connectors. Components provide and require interfaces. Interfaces can be attached to ports, which control component interactions. Connectors link components through ports or interfaces.
- A deployment diagram models the physical deployment of artifacts across nodes like hardware. It shows the configuration of runtime processing nodes and the artifacts deployed on them, such as executable files, libraries, and tables.
Sales & Operations Planning (S&OP): An IntroductionSteelwedge
ย
Do you know the secret to a successful Sales and Operations Planning process?
Your ability to troubleshoot issues, plan for unexpected events, and maintain a reliable, single set of planning numbers is drastically affected by people, process and technology.
Educate your colleagues or refresh your own skills with the new introduction to S&OP presentation.
For more information about S&OP and how Steelwedge can help your business, please visit: http://www.steelwedge.com/resources/sales-and-operations-planning-intro/
The document discusses various machine learning clustering algorithms like K-means clustering, DBSCAN, and EM clustering. It also discusses neural network architectures like LSTM, bi-LSTM, and convolutional neural networks. Finally, it presents results from evaluating different chatbot models on various metrics like validation score.
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 studentโs Deep LearningNAVER D2
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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.
[234]Fast & Accurate Data Annotation Pipeline for AI applicationsNAVER D2
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This document provides a summary of new datasets and papers related to computer vision tasks including object detection, image matting, person pose estimation, pedestrian detection, and person instance segmentation. A total of 8 papers and their associated datasets are listed with brief descriptions of the core contributions or techniques developed in each.
[226]NAVER ๊ด๊ณ deep click prediction: ๋ชจ๋ธ๋ง๋ถํฐ ์๋น๊น์งNAVER D2
ย
This document presents a formula for calculating the loss function J(ฮธ) 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 ฮป that penalizes predicted embeddings being dissimilar from actual embeddings. It also defines the cosine similarity term used in the regularization.
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++.
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.
26. Web accessibility refers to the inclusive practice of making websites usable
by people of all abilities and disabilities. Wikipediaยฎ
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