In this deeplizard episode, learn how to prepare and process our own custom data set of sign language digits, which will be used to train our fine-tuned MobileNet model in a future episode.

VIDEO SECTIONS

  • 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
  • 00:40 Obtain the Data
  • 01:30 Organize the Data
  • 09:42 Process the Data
  • 13:11 Collective Intelligence and the DEEPLIZARD HIVEMIND

deeplizard  introduces MobileNets, a class of light weight deep convolutional neural networks that are vastly smaller in size and faster in performance than many other popular models.

VIDEO SECTIONS

  • 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
  • 00:17 Intro to MobileNets
  • 02:56 Accessing MobileNet with Keras
  • 07:25 Getting Predictions from MobileNet
  • 13:32 Collective Intelligence and the DEEPLIZARD HIVEMIND

In this video, Mandy from deeplizard  demonstrates how to use the fine-tuned VGG16 Keras model that we trained in the last episode to predict on images of cats and dogs in our test set.

Index:

  • 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
  • 00:17 Predict with a Fine-tuned Model
  • 05:40 Plot Predictions With A Confusion Matrix
  • 05:16 Collective Intelligence and the DEEPLIZARD HIVEMIND

In this tutorial, see how you can train a Convolutional Neural Network in PyTorch and convert it into an ONNX model.

Once the model is in in ONNX format, you can import that into other frameworks such as TensorFlow for either inference and reusing the model through transfer learning.

This post is the third in a series of introductory tutorials on the Open Neural Network Exchange (ONNX), an initiative from AWS, Microsoft, and Facebook to define a standard for interoperability across machine learning platforms. See: Part 1 , Part 2 . In this tutorial, we will train a […]