TensorFlow: Convolutional Neural Networks for Image Classification

TensorFlow    |    Intermediate
  • 17 Videos | 1h 29m 12s
  • Includes Assessment
  • Earns a Badge
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Examine how to work with Convolutional Neural Networks, and discover how to leverage TensorFlow to build custom CNN models for working with images.

WHAT YOU WILL LEARN

  • compare the working of the visual cortex with a neural network
    apply convolution to an input matrix and generate a result
    use scikit-image to read in an image
    instantiate a convolutional kernel to use with a convolutional layer
    work with convolutional layers to detect edges in the input image
    recognize how pooling works and its use in a convolutional neural network
    recognize how hyperparameters are used to design the convolutional neural network
    identify the standard structure of a convolutional neural network
  • define an overfitted model and the bias-variance trade-off
    identify regularization, cross-validation, and dropout as ways to mitigate overfitting
    describe how to use the CIFAR-10 dataset for image classification
    demonstrate how to split the dataset into training and test images
    create placeholders and variables for the convolutional neural network
    define convolutional and pooling layers programmatically
    demonstrate how to run training and prediction on the CIFAR-10 dataset
    define the role of convolutional and pooling layers in a convolutional neural network

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 44s
    UP NEXT
  • Playable
    2. 
    The Visual Cortex
    3m 19s
  • Locked
    3. 
    Convolution and Convolutional Layers
    7m 15s
  • Locked
    4. 
    Image as an Input Matrix
    2m 55s
  • Locked
    5. 
    Convolution Kernel and Convolutional Layer
    6m 20s
  • Locked
    6. 
    Edge Detection Using Convolution
    4m 18s
  • Locked
    7. 
    Pooling and Pooling Layers
    5m 8s
  • Locked
    8. 
    Zero-Padding and Stride Size
    3m 4s
  • Locked
    9. 
    Convolutional Neural Network Architecture
    5m 35s
  • Locked
    10. 
    Overfitting and the Bias-Variance Trade-Off
    7m 48s
  • Locked
    11. 
    Preventing Overfitting
    3m 59s
  • Locked
    12. 
    The CIFAR-10 Dataset
    5m 58s
  • Locked
    13. 
    Training and Test Dataset for Image Classification
    3m 45s
  • Locked
    14. 
    Placeholders and Variables for the CNN
    3m 51s
  • Locked
    15. 
    CNN for Image Classification
    8m 1s
  • Locked
    16. 
    Train and Predict Using a CNN
    3m 45s
  • Locked
    17. 
    Exercise: Working with CNNs
    4m 57s

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