Convolutional Neural Networks: Implementing & Training

Neural Networks    |    Intermediate
  • 8 videos | 30m 12s
  • Includes Assessment
  • Earns a Badge
Rating 4.0 of 6 users Rating 4.0 of 6 users (6)
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

  • 1m 38s
  • 4m 2s
    In this video, find out how to implement an image recognition classifier using convolutional neural networks, Keras, and TensorFlow. FREE ACCESS
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    3.  ResNet Layers
    3m 32s
    Upon completion of this video, you will be able to describe ResNet layers and blocks. FREE ACCESS
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    4.  PyTorch Ecosystem
    2m 54s
    After completing this video, you will be able to list the essential PyTorch ecosystem tools that can be used to develop and implement neural networks. FREE ACCESS
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    5.  Install and Configure PyTorch
    3m 13s
    During this video, you will learn how to install and configure PyTorch. FREE ACCESS
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    6.  CNN Using PyTorch
    7m
    During this video, you will learn how to implement convolutional neural networks using PyTorch. FREE ACCESS
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    7.  Training CNN
    2m 26s
    During this video, you will learn how to use PyTorch to train convolutional neural networks (CNNs) to categorize images of clothing. FREE ACCESS
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    8.  Exercise: Implementing CNNs with PyTorch
    5m 29s
    In this video, you will learn how to install and configure PyTorch and implement convolutional neural networks (CNNs) using PyTorch. FREE ACCESS

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