This document lists various projects in different categories such as Hollow MicroLoan Charity Box, Disney Animated, DeviantART, CareSync, Fort McMoney, An Idea Lives On, Aco Virtual, The Little Red Logo that Transformed the Marriage Equality Narrative, Heart of the High Country, and Remote Control Tourist. The projects cover topics including charity, animation, art, healthcare, experimental films, documentaries, music, personal work, student work, and technical achievement.
This document lists various projects in different categories such as Hollow MicroLoan Charity Box, Disney Animated, DeviantART, CareSync, Fort McMoney, An Idea Lives On, Aco Virtual, The Little Red Logo that Transformed the Marriage Equality Narrative, Heart of the High Country, and Remote Control Tourist. The projects cover topics including charity, animation, art, healthcare, experimental films, documentaries, music, personal work, student work, and technical achievement.
RxJS is a library for composing asynchronous and event-based programs by using observable sequences. It provides operators that allow transforming, filtering, and combining streams of data from diverse sources. Key features include:
- Representing asynchronous data streams with Observables
- Providing LINQ-like operators for querying and transforming streams
- Using Schedulers to control concurrency and synchronize streams with other asynchronous operations like user interactions, server requests, etc.
La endocarditis es una enfermedad que se produce como resultado de la inflamaci坦n del endocardio, es decir, un proceso inflamatorio localizado en el endocardio. toado de guias espa単olas y colombianas
An immersive workshop at General Assembly, SF. I typically teach this workshop at General Assembly, San Francisco. To see a list of my upcoming classes, visit https://generalassemb.ly/instructors/seth-familian/4813
I also teach this workshop as a private lunch-and-learn or half-day immersive session for corporate clients. To learn more about pricing and availability, please contact me at http://familian1.com
JMI Techtalk: 螳 - Toward tf.keras from tf.estimator - From TensorFlow 2.0 p...Lablup Inc.
油
Techtalk TensorFlow 2.0朱 伎 tf.estimator tf.keras襦 伎伎 伎 る.
This Techtalk explains why you need to migrate from tf.estimator to tf.keras when moving to TensorFlow 2.0.
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 students 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 蟯螻 deep click prediction: 覈碁覿 觜蟾讌NAVER D2
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This document presents a formula for calculating the loss function J(慮) 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 了 that penalizes predicted embeddings being dissimilar from actual embeddings. It also defines the cosine similarity term used in the regularization.
[214] Ai Serving Platform: 襭 蟇伎 誤朱一るゼ 豌襴蠍 螻蟲磯蠍NAVER D2
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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.
2. 豢豌
WWDC 2014 る誤
WWDC 2014 402, 403, 404, 407
The Swift Programming Language
Using Swift with Cocoa and Objective-C
https://medium.com/swift-programming/
21. Chris Lattner
2000 朱Μ語 LLVM
2005 襯
2010 Swift 螳覦
2011 螳覦れ 襯, 2013 螳覦 伎 譯殊
貉れり
Objective-C, Rust, Haskell, Ruby, Python, C#, CLU,
and far too many others to list. (Chris Lattner)
44. Optional Binding
println(My age is, (age!));
//age螳 nil企
var age:Int? = mySomeString.toInt()
if let ageValue = age {
println( (ageValue) も)
} else {
println( 企讌 給も)
}
45. Optional Binding
if let age = mySomeString.toInt() {
println( (age) も)
} else {
println( 企讌 給も)
}