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GitHub - googlecreativelab/teachable-machine-boilerplate: Boilerplate code for Teachable Machine
This is a small boilerplate project that demonstrates how to use deeplearn.js to create projects like Teachable Machine. The code shows how you can create setup a KNN classifier that can be trained live in the browser on a webcam image. It is intentionally kept very simple so it can provide a starting point for new projects.
machine-learning  deep-learning  javascript  p5js  KNN  neural-network  deeplearn.js  github 
5 hours ago by squishyrobot
K-Means Clustering in Python - Added October 03, 2017 at 10:43AM
algorithms  machine-learning  python  read2of 
10 hours ago by xenocid
302 Found
DevOps Pipeline for a Machine Learning Project - Added October 03, 2017 at 10:43AM
ci-cd  data-engineering  devops  machine-learning  read2of 
10 hours ago by xenocid
[1801.05895] Sparsely Connected Convolutional Networks
Residual learning with skip connections permits training ultra-deep neural networks and obtains superb performance. Building in this direction, DenseNets proposed a dense connection structure where each layer is directly connected to all of its predecessors. The densely connected structure leads to better information flow and feature reuse. However, the overly dense skip connections also bring about the problems of potential risk of overfitting, parameter redundancy and large memory consumption. In this work, we analyze the feature aggregation patterns of ResNets and DenseNets under a uniform aggregation view framework. We show that both structures densely gather features from previous layers in the network but combine them in their respective ways: summation (ResNets) or concatenation (DenseNets). We compare the strengths and drawbacks of these two aggregation methods and analyze their potential effects on the networks' performance. Based on our analysis, we propose a new structure named SparseNets which achieves better performance with fewer parameters than DenseNets and ResNets.
neural-networks  binge-purge-cycles  architecture  algorithms  machine-learning  representation  to-write-about 
yesterday by Vaguery

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