Neural Networks and Convolutional Neural Networks Essential Training

Deep Learning and Neural Networks

Course details

  • 1h 19m
  • Intermediate
  • Released: 5/4/2018

Take a deep dive into neural networks and convolutional neural networks, two key concepts in the area of machine learning. In this hands-on course, instructor Jonathan Fernandes covers fundamental neural and convolutional neural network concepts. Jonathan begins by providing an introduction to the components of neural networks, discussing activation functions and backpropagation. He then looks at convolutional neural networks, explaining why they’re particularly good at image recognition tasks. He also steps through how to build a neural network model using Keras. Plus, learn about VGG16, the history of the ImageNet challenge, and more.

Learning objectives

  • Neurons and artificial neurons
  • Components of neural networks
  • Neural network visualization
  • Neural network implementation in Keras
  • Compiling and training a neural network model
  • Accuracy and evaluation of a neural network model
  • Convolutional neural networks in Keras
  • Enhancements to convolutional neural networks
  • Working with VGG16
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