Understanding and Visualizing Convolutional Neural Networks (CNNs)
In this free online course, learn about the role of convolutional neural networks in processing images.
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Convolution neural networks (CNNs) are mainly used for image classification and recognition because of their high accuracy. The course introduces the need for using CNNs in the processing of images. You will explore the role of CNNs in conquering the difficulties presented by other networks in sifting through images. Following this, the function of the backpropagation algorithm in CNNs is discussed, where you will be taught about the mathematical notations of a backpropagation algorithm. The evolution of CNN architectures over the last few decades is also highlighted. This will include the various types of architectures and the process of selecting the appropriate architecture based on the task at hand. This course explains how CNN architectures are used to extract information classes from a multiband raster image to create thematic maps.
Next, the course highlights the various ways for improving the CNN architectures based on earlier works. This will incorporate the elements of contemporary CNN models that have been created for extending the assistance beyond other networks. You will explore the various architectural implementations that led from the MobileNet architecture to state-of-the-art EfficientNet architecture, with the many improvements in between. The course explains the importance of visualizing a CNN model. This will include the various methods of envisioning and presenting the model predictions of a CNN. In addition to this, the course will outline the applications of these neural networks in processing images. You will discover how CNN, using the deep learning technique, outperforms other methods in analyzing images. The processes for interpreting and understanding the applications of CNNs in analyzing images are also explained.
Finally, you will be taught how visual explanations are extracted from deep networks through gradient-based localization. This will include the role of class activation maps in enhancing the clarifications for profound CNNs. The current methods for explaining CNNs are highlighted. You will explore the interpretable modes and game-theoretic backgrounds used for images, texts and tabular data for real-world experiments. Lastly, you will be taught about the various approaches to addressing the problem of saturating gradients. This will include solving a problem that refers to a function for which a more considerable input will not lead to a relevant increase in output. This is an exciting course that will interest those studying computer science or those interested in these topics. Why wait? Sign up today and start learning about the application of convolutional neural networks to various computer vision tasks.
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Introduction to Convolutional Neural Networks
Introduction to Convolutional Neural Networks – Learning Outcomes
An Overview of Convolutional Neural Networks
Backpropagation in Convolutional Neural Networks
CNN Architectures for Image Classification
Recent CNN Architectures
Fine-Tuning in CNN
Introduction to Convolutional Neural Networks – Lesson Summary
Visualization and Understanding CNN's
Visualization and Understanding CNN's – Learning Outcomes
Methods of CNN Visualization
Understanding the Methods of CNN
Class Attribution Map Methods
Recent Methods for Explaining CNN's
Beyond Explaining CNN's
Visualization and Understanding CNN's – Lesson Summary
By the end of this course, you will be able to:
- Outline the role of convolutional neural networks (CNN) in analyzing images
- Explain the various types of CNNs
- Discuss the application of backpropagation in CNNs
- Identify the different CNN architectures used for image classification
- Describe the process of fine-tuning CNN architecture
- Outline the multiple approaches for visualizing CNNs
- Describe the methods for interpreting and understanding the applications of CNNs
- Explain the features of CNNs in image classification
- Identify the prospects of the various components for discriminative localization
- Summarize the recent methods for explaining CNNs
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