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How can CNNs be used for tasks other than image classification, such as object detection or segmentation? Convolution Neural Network (CNN) Input Pooling Pooling Pooling Kernel Convolution ReLU Convolution Convolution ReLU ReLU Flatten Layer Feature Maps- Fully Connected Layer Feature Extraction Output 02 Horse -Zebra Dog SoftMax Activation Function Classification Probabilistic Distribution TYPES OF NEURAL NETWORKS FEEDFORWARD NEURAL NETWORKS Feedforward neural networks are good at solving problems with a clear ndationship between the input and the output but may not be as effective at figuring out more complex relationships. 回 CONVOLUTIONAL NEURAL NETWORKS Convolutional neural networks are used for tasks that involve data with a gild-like structure, such as image recognition, but may require a large amount of data and be slow. RECURRENT NEURAL NETWORKS Recurrent neural networks are used fortasks involving data in a sequence, such as a language translation and speech recognition, but they may need help learning long term relationships, which can be challenging to train. GENERATIVE ADVERSARIAL NETWORKS Generative adversarial networks are composed of two neutel networks that work together to generate synthetic data that appears real but may be chalenging to train anc recuire a large amount of data to perform well. They have been used for tasks such as crating nalistic images and AUTOENCODER NEURAL NETWORKS Autoencoders are used to reduce the complexity of data and learn important features, but they may be sorsitive to the settings used and may not always leam meaningful patterns in the data. They have been applied in tasks such as image and speech recognition

How can CNNs be used for tasks other than image classification, such as object detection or segmentation? Convolution Neural Network (CNN) Input Pooling Pooling Pooling Kernel Convolution ReLU Convolution Convolution ReLU ReLU Flatten Layer Feature Maps- Fully Connected Layer Feature Extraction Output 02 Horse -Zebra Dog SoftMax Activation Function Classification Probabilistic Distribution TYPES OF NEURAL NETWORKS FEEDFORWARD NEURAL NETWORKS Feedforward neural networks are good at solving problems with a clear ndationship between the input and the output but may not be as effective at figuring out more complex relationships. 回 CONVOLUTIONAL NEURAL NETWORKS Convolutional neural networks are used for tasks that involve data with a gild-like structure, such as image recognition, but may require a large amount of data and be slow. RECURRENT NEURAL NETWORKS Recurrent neural networks are used fortasks involving data in a sequence, such as a language translation and speech recognition, but they may need help learning long term relationships, which can be challenging to train. GENERATIVE ADVERSARIAL NETWORKS Generative adversarial networks are composed of two neutel networks that work together to generate synthetic data that appears real but may be chalenging to train anc recuire a large amount of data to perform well. They have been used for tasks such as crating nalistic images and AUTOENCODER NEURAL NETWORKS Autoencoders are used to reduce the complexity of data and learn important features, but they may be sorsitive to the settings used and may not always leam meaningful patterns in the data. They have been applied in tasks such as image and speech recognition

Advanced Engineering Mathematics
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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How can CNNs be used for tasks other than image classification, such as object detection or segmentation? Convolution Neural Network (CNN) Input Pooling Pooling Pooling Kernel Convolution ReLU Convolution Convolution ReLU ReLU Flatten Layer Feature Maps- Fully Connected Layer Feature Extraction Output 02 Horse -Zebra Dog SoftMax Activation Function Classification Probabilistic Distribution TYPES OF NEURAL NETWORKS FEEDFORWARD NEURAL NETWORKS Feedforward neural networks are good at solving problems with a clear ndationship between the input and the output but may not be as effective at figuring out more complex relationships. 回 CONVOLUTIONAL NEURAL NETWORKS Convolutional neural networks are used for tasks that involve data with a gild-like structure, such as image recognition, but may require a large amount of data and be slow. RECURRENT NEURAL NETWORKS Recurrent neural networks are used fortasks involving data in a sequence, such as a language translation and speech recognition, but they may need help learning long term relationships, which can be challenging to train. GENERATIVE ADVERSARIAL NETWORKS Generative adversarial networks are composed of two neutel networks that work together to generate synthetic data that appears real but may be chalenging to train anc recuire a large amount of data to perform well. They have been used for tasks such as crating nalistic images and AUTOENCODER NEURAL NETWORKS Autoencoders are used to reduce the complexity of data and learn important features, but they may be sorsitive to the settings used and may not always leam meaningful patterns in the data. They have been applied in tasks such as image and speech recognition
Transcribed Image Text:How can CNNs be used for tasks other than image classification, such as object detection or segmentation? Convolution Neural Network (CNN) Input Pooling Pooling Pooling Kernel Convolution ReLU Convolution Convolution ReLU ReLU Flatten Layer Feature Maps- Fully Connected Layer Feature Extraction Output 02 Horse -Zebra Dog SoftMax Activation Function Classification Probabilistic Distribution TYPES OF NEURAL NETWORKS FEEDFORWARD NEURAL NETWORKS Feedforward neural networks are good at solving problems with a clear ndationship between the input and the output but may not be as effective at figuring out more complex relationships. 回 CONVOLUTIONAL NEURAL NETWORKS Convolutional neural networks are used for tasks that involve data with a gild-like structure, such as image recognition, but may require a large amount of data and be slow. RECURRENT NEURAL NETWORKS Recurrent neural networks are used fortasks involving data in a sequence, such as a language translation and speech recognition, but they may need help learning long term relationships, which can be challenging to train. GENERATIVE ADVERSARIAL NETWORKS Generative adversarial networks are composed of two neutel networks that work together to generate synthetic data that appears real but may be chalenging to train anc recuire a large amount of data to perform well. They have been used for tasks such as crating nalistic images and AUTOENCODER NEURAL NETWORKS Autoencoders are used to reduce the complexity of data and learn important features, but they may be sorsitive to the settings used and may not always leam meaningful patterns in the data. They have been applied in tasks such as image and speech recognition
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