Convolutional Neural Networks: Implementing & Training

Neural Networks    |    Intermediate
  • 8 Videos | 33m 12s
  • Includes Assessment
  • Earns a Badge
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This course explores machine learning convolutional neural networks (CNNs), which are popular for implementation in image and audio processing. Learners explore AI (artificial intelligence), and the issues surrounding implementation, how to approach organizational talent and strategy, and how to prepare for AI architecture in this 8-video course. You will learn to use the Google Colab tool, and to implement image recognition classifier by using CNN, Keras, and TensorFlow. Next, learn to install and implement a model, and use it for image classification. You will examine the artificial neural network ResNet (residual neural network), and how it builds on constructs known from pyramidal cells and cerebral cortex. You will also study PyTorch, an open-source machine learning library that enables fast, flexible experimentation, and efficient production through a hybrid front end, and learn to use the PyTorch ecosystem tool to develop and implement neural networks. Finally, this course demonstrates how to create a data set by using Training CNN by using PyTorch to categorize garments.

WHAT YOU WILL LEARN

  • implement image recognition classifier using convolutional neural networks, Keras, and TensorFlow
    describe ResNet layers and blocks
    list the essential PyTorch ecosystem tools that can be used to develop and implement neural networks
    install and configure PyTorch
  • implement convolutional neural networks (CNNs) using PyTorch
    use PyTorch to train convolutional neural networks (CNNs) to categorize garments
    install and configure PyTorch and implement convolutional neural networks (CNNs) using PyTorch

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 38s
    UP NEXT
  • Playable
    2. 
    Image Recognition
    4m 2s
  • Locked
    3. 
    ResNet Layers
    3m 32s
  • Locked
    4. 
    PyTorch Ecosystem
    2m 54s
  • Locked
    5. 
    Install and Configure PyTorch
    3m 13s
  • Locked
    6. 
    CNN Using PyTorch
    7m
  • Locked
    7. 
    Training CNN
    2m 26s
  • Locked
    8. 
    Exercise: Implementing CNNs with PyTorch
    5m 29s

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