Deep Learning in Clojure with Cortex

Deep Learning in Clojure With Cortex

  • The training data consists of 25,000 images of cats and dogs.
  • It reads in the external nippy file that contains the trained network description, takes a random image from the testing directory, and classifies it.
  • We want all the dog images to be under a “dog” directory and the cat images under the “cat” directory so that the all the indexed images under them have the correct “label”.
  • How many times it thought a cat was really a cat and how many times it got it wrong.
  • We need all the images to be the same size as well as in a directory structure that is split up into the training and test images.

There is an awesome new Clojure-first machine learning library called Cortex that was open sourced recently. I’ve been exploring it lately and …
Continue reading “Deep Learning in Clojure with Cortex”

GitHub

  • cifar-multi : Cifar10 classification using a convolutional neural network, where two independent learned optimizers are used.
  • learning_rate : Learning rate, only relevant if using Adam optimizer.
  • evaluation_period : Epochs before the optimizer is evaluated.
  • second_derivatives : If true , the optimizer will try to compute second derivatives through the loss function specified by the problem.
  • cifar : Cifar10 classification using a convolutional neural network.

learning-to-learn – Learning to Learn in TensorFlow
Continue reading “GitHub”

A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1

Understanding Convolutional Neural Networks Part 1  via @kdnuggets #DataScience #deeplearning

  • The filters on the first layer convolve around the input image and “activate” (or compute high values) when the specific feature it is looking for is in the input volume.
  • Remember, what we have to do is multiply the values in the filter with the original pixel values of the image.
  • As the filter is sliding, or convolving , around the input image, it is multiplying the values in the filter with the original pixel values of the image (aka computing element wise multiplications ).
  • Image classification is the task of taking an input image and outputting a class (a cat, dog, etc) or a probability of classes that best describes the image.
  • When a computer sees an image (takes an image as input), it will see an array of pixel values.


Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.

Continue reading “A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1”

A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1

A Beginner’s Guide To Understanding Convolutional #NeuralNetworks Part 1  #DeepLearning

  • The filters on the first layer convolve around the input image and “activate” (or compute high values) when the specific feature it is looking for is in the input volume.
  • Remember, what we have to do is multiply the values in the filter with the original pixel values of the image.
  • As the filter is sliding, or convolving , around the input image, it is multiplying the values in the filter with the original pixel values of the image (aka computing element wise multiplications ).
  • Image classification is the task of taking an input image and outputting a class (a cat, dog, etc) or a probability of classes that best describes the image.
  • When a computer sees an image (takes an image as input), it will see an array of pixel values.


Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.

Continue reading “A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1”

Laws, Sausages and ConvNets

Laws, Sausages and #ConvNets - great overview #DeepLearning #NeuralNets

  • Convolution is a simple mathematical operation, so the enormous complexity involved in implementing convolutional layers may be surprising.
  • Instead of dealing with networks, I take the point of view that a convolutional layer is simply a differentiable function.
  • Full-blown ConvNets may incorporate a variety of ideas and mechanisms, but in the following I’m going to focus on their very core: convolutional layers.
  • Convolutional Neural Networks (CNNs or ConvNets in short) give the state-of-the-art results in many problem domains.
  • The post is about the nuts and bolts: algorithms, implementations and optimizations.

Read the full article, click here.


@kdnuggets: “Laws, Sausages and #ConvNets – great overview #DeepLearning #NeuralNets”


Laws, like sausages, cease to inspire respect in proportion as we know howthey are made.


Laws, Sausages and ConvNets