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Decision Tree in Machine Learning
A decision tree is a flowchart-like structure in which each internal node represents a test on a feature (e.g. whether a coin flip comes up heads or tails), each leaf node represents a class label (decision taken after computing all features) and branches represent conjunctions of features that lead to those class labels. The paths from root to leaf represent classification rules. Below diagram illustrate the basic flow of decision tree for decision making with labels (Rain(Yes), No Rain(No)).
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Five Lessons From the First Three Years of Michelangelo
Uber has been one of the most active contributors to open source machine learning technologies in the last few years. While companies like Google or Facebook have focused their contributions in new deep learning stacks like TensorFlow, Caffe2 or PyTorch, the Uber engineering team has really focused on tools and best practices for building machine learning at scale in the real world. Technologies such as Michelangelo, Horovod, PyML, Pyro are some of examples of Uber’s contributions to the machine learning ecosystem.
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Real Time Facial Expression Recognition
Computer animated agents and robots bring new dimension in human computer interaction which makes it vital as how computers can affect our social life in day-to-day activities. Face to face communication is a real-time process operating at a time scale in the order of milliseconds. The level of uncertainty at this time scale is considerable, making it necessary for humans and machines to rely on sensory rich perceptual primitives rather than slow symbolic inference processes.
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Meet TiDB: An open source NewSQL database
TiDB is an open source NewSQL database released under the Apache 2.0 License. Because it speaks the MySQL protocol, your existing applications will be able to connect to it using any MySQL connector, and most SQL functionality remains identical (joins, subqueries, transactions, etc.). Step under the covers, however, and thereare differences.
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How plants evolved to make ants their servants
Plants are boring. They just sit there photosynthesizing while animals have all the fun. Right?
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Accurate Online Speaker Diarization with Supervised Learning
Speaker diarization, the process of partitioning an audio stream with multiple people into homogeneous segments associated with each individual, is an important part of speech recognition systems. By solving the problem of “who spoke when”, speaker diarization has applications in many important scenarios, such as understanding medical conversations, video captioning and more. However, training these systems with supervised learning methods is challenging — unlike standard supervised classification tasks, a robust diarization model requires the ability to associate new individuals with distinct speech segments that weren’t involved in training.
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The next version of HTTP won’t be using TCP
Today’s HTTP (versions 1.0, 1.1, and 2) are all layered on top of TCP (Transmission Control Protocol). TCP, defined as part of the core set of IP (Internet Protocol) layers, provides reliable, ordered, and error-checked delivery of data over an IP network. ‘Reliable’ means that if some data goes missing during transfer (due to a hardware failure, congestion, or a timeout), the receiving end can detect this and demand that the sending end re-send the missing data; ‘ordered’ means that data is received in the order that it was transmitted in; ‘error-checked’ means that any corruption during transmission can be detected.
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