TensorFlow: Convolutional Neural Networks for Image Classification

TensorFlow    |    Intermediate
  • 17 videos | 1h 21m 42s
  • 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

  • 1m 44s
  • 3m 19s
    In this video, learn how to compare the working of the visual cortex with a neural network. FREE ACCESS
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    3.  Convolution and Convolutional Layers
    7m 15s
    In this video, you will learn how to apply convolution to an input matrix and generate an output. FREE ACCESS
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    4.  Image as an Input Matrix
    2m 55s
    In this video, you will learn how to use scikit-image to read an image. FREE ACCESS
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    5.  Convolution Kernel and Convolutional Layer
    6m 20s
    During this video, you will learn how to create a convolutional kernel to use with a convolutional layer. FREE ACCESS
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    6.  Edge Detection Using Convolution
    4m 18s
    Find out how to work with convolutional layers to detect edges in the input image. FREE ACCESS
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    7.  Pooling and Pooling Layers
    5m 8s
    Upon completion of this video, you will be able to recognize how pooling works and its use in a convolutional neural network. FREE ACCESS
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    8.  Zero-Padding and Stride Size
    3m 4s
    Upon completion of this video, you will be able to recognize how to use hyperparameters to design the convolutional neural network. FREE ACCESS
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    9.  Convolutional Neural Network Architecture
    5m 35s
    In this video, you will identify the standard structure of a convolutional neural network. FREE ACCESS
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    10.  Overfitting and the Bias-Variance Trade-Off
    7m 48s
    Find out how to define an overfitted model and what the bias-variance trade-off is. FREE ACCESS
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    11.  Preventing Overfitting
    3m 59s
    In this video, you will identify regularization, cross-validation, and dropout as ways to reduce overfitting. FREE ACCESS
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    12.  The CIFAR-10 Dataset
    5m 58s
    Upon completion of this video, you will be able to describe how to use the CIFAR-10 dataset for image classification. FREE ACCESS
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    13.  Training and Test Dataset for Image Classification
    3m 45s
    In this video, you will learn how to split the dataset into training and testing images. FREE ACCESS
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    14.  Placeholders and Variables for the CNN
    3m 51s
    During this video, you will learn how to create placeholders and variables for the convolutional neural network. FREE ACCESS
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    15.  CNN for Image Classification
    8m 1s
    In this video, you will learn how to define convolutional and pooling layers programmatically. FREE ACCESS
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    16.  Train and Predict Using a CNN
    3m 45s
    In this video, you will learn how to run training and prediction on the CIFAR-10 dataset. FREE ACCESS
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    17.  Exercise: Working with CNNs
    4m 57s
    In this video, you will learn how to define the role of convolutional and pooling layers in a convolutional neural network. FREE ACCESS

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