Convo Nets for Visual Recognition: Filters and Feature Mapping in CNN
Intermediate
- 13 videos | 1h 6m 32s
- Includes Assessment
- Earns a Badge
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
-
1m 14s
-
6m 19sAfter completing this video, you will be able to recognize the capabilities and features of convolutional networks that make it a recommended choice for visual recognition implementation. FREE ACCESS
-
3. Convo Nets Architecture and Layers4m 47sUpon completion of this video, you will be able to illustrate the architecture and the various layers of convolutional neural networks. FREE ACCESS
-
4. Filters and Their Usage4m 46sLearn how to 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. FREE ACCESS
-
5. Filters with Keras5m 41sIn this video, you will learn about the approach of using Keras to visualize inputs that maximize the activation of filters in different layers of convolutional neural networks. FREE ACCESS
-
6. Feature Map2m 37sIn this video, you will learn how to define the concept of feature map in convolutional networks and how to illustrate the approach of visualizing feature maps. FREE ACCESS
-
7. Plotting Feature Map with Python7m 12sDuring this video, you will learn how to plot the feature map of the first convolutional layer for given images and visualize the feature map output from every block in the VGG model. FREE ACCESS
-
8. Optimization Parameters4m 27sIn this video, find out how to identify the critical parameters that we need to tune to optimize convolutional neural networks. FREE ACCESS
-
9. Hyperparameters Tuning6m 19sUpon completion of this video, you will be able to recall the essential hyperparameters that are applied to convolutional networks for optimization and model refinement. FREE ACCESS
-
10. Tuning Hyperparameters with TensorFlow and Keras9m 5sIn this video, you will work with hyperparameters to derive optimized convolutional network models using Keras and TensorFlow. FREE ACCESS
-
11. Pooling Layer3m 49sAfter completing this video, you will be able to recognize the role of the pooling layer in convolutional networks along with the various operations and functions that we can apply to the layer. FREE ACCESS
-
12. Implementing Pooling Layer6m 1sIn this video, you will learn how to implement a convolutional and pooling layer in Python. FREE ACCESS
-
13. Exercise: Plotting Feature Map4m 15sAfter completing this video, you will be able to recall the various layers of convolutional networks, plot the feature map of the first convolutional layer for a given image, and visualize the Feature map output from every block in the VGG model. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.
Digital badges are yours to keep, forever.YOU MIGHT ALSO LIKE
