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Training a machine learning model with sparse data sometimes requires data augmentation, or tricking the model into thinking that the same image is something new, in order for it to learn to recognize patterns from different perspectives of the same image. There are a number of different types of image…


Hyperparameter Optimization using bayesian optimization | LaptrinhX

Write a python script that optimizes a machine learning model of your choice using GPyOpt:

  • Your script should optimize at least 5 different hyperparameters. E.g. learning rate, number of units in a layer, dropout rate, L2 regularization weight, batch size
  • Your model should be optimized on a single satisficing metric

Abstract

This will be my first attempt to build a successful classifier on the CIFAR 10 dataset through experimentation with various models and optimization techniques, having never sought to tackle a machine learning problem without at least a little bit of guidance. The goal is to take a pretrained model, and…


Researchers at the University of Toronto improved state of the art models for classifying under the ImageNet dataset. Krizhevsky et. al.‘s 2012 paper ImageNet Classification with Deep Convolutional Neural Networks describes their research. …


  • L1 regularization
  • L2 regularization
  • Dropout
  • Data Augmentation
  • Early Stopping

Neural networks use activation functions to determine whether each node in the network should be activated or not, returning a non-zero values for the output of the layer. Given a certain set of inputs, the neuron output is either activated or not depending on the type of activation function, and…


Gradient descent is the process of updating the weights for the nodes in each layer of a neural network during back propagation in order to reduce the error in the predicted values to maximize the accuracy of the model. This is the “learning” process in supervised machine learning. You train…


TLDR; here’s a snapshot of what you need in ~/.vimrc:

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