Convo Nets for Visual Recognition: Filters and Feature Mapping in CNN

  • 13 Videos | 1h 12m 2s
  • Includes Assessment
  • Earns a Badge
Likes 1 Likes 1
In this 13-video course, you will explore the capabilities and features of convolutional networks for machine learning that make it a recommended choice for visual recognition implementation. Begin by examining the architecture and the various layers of convolutional networks, including pooling layer, convo layer, normalization layer, and fully connected layer, and defining the concept and types of filters in convolutional networks along with their usage scenarios. Learn about the approach to maximizing filter activation with Keras; define the concept of feature map in convolutional networks and illustrate the approach of visualizing feature maps; and plot the map of the first convo layer for given images, then visualize the feature map output from every block in the visual geometry group (VGG) model. Look at optimization parameters for convolutional networks, and hyperparameters for tuning and optimizing convolutional networks. Learn about applying functions on pooling layer; pooling layer operations; implementing pooling layer with Python, and implementing convo layer with Python. The concluding exercise involves plotting feature maps.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    recognize the capability and features of convolutional networks that makes it a recommended choice for visual recognition implementation
    illustrate the architecture and the various layers of convolutional networks
    define the concept and types of filters in convolutional networks along with their usage scenarios to depict the impact of filters on feature sets during the training process
    demonstrate the approach of using Keras to visualize inputs that maximize the activation of filters in different layers of convolutional networks
    define the concept of feature map in convolutional networks and illustrate the approach of visualizing feature maps
    plot the feature map of the first convo layer for given images and visualize the feature map output from every block in the VGG model
  • identify the critical parameters that we need to tune to optimize convolutional networks
    recall the essential hyperparameters that are applied on convolutional networks for optimization and model refinement
    work with hyperparameters using Keras and TensorFlow to derive optimized convolutional network models
    recognize the role of pooling layer in convolutional networks along with the various operations and functions that we can apply on the layer
    demonstrate how to implement convo and pooling layer in Python
    recall the various layers of convolutional networks, plot the feature map of the first convo layer for a given image and visualize the Feature map output from every block in the VGG model

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 14s
    UP NEXT
  • Playable
    2. 
    Convolutional Networks
    6m 19s
  • Locked
    3. 
    Convo Nets Architecture and Layers
    4m 47s
  • Locked
    4. 
    Filters and Their Usage
    4m 46s
  • Locked
    5. 
    Filters with Keras
    5m 41s
  • Locked
    6. 
    Feature Map
    2m 37s
  • Locked
    7. 
    Plotting Feature Map with Python
    7m 12s
  • Locked
    8. 
    Optimization Parameters
    4m 27s
  • Locked
    9. 
    Hyperparameters Tuning
    6m 19s
  • Locked
    10. 
    Tuning Hyperparameters with TensorFlow and Keras
    9m 5s
  • Locked
    11. 
    Pooling Layer
    3m 49s
  • Locked
    12. 
    Implementing Pooling Layer
    6m 1s
  • Locked
    13. 
    Exercise: Plotting Feature Map
    4m 15s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platform

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE