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CNN – Full CNN Architecture
CNN – Full CNN Architecture Syllabus: Architecture Diagram Of CNN. Input Layer Of CNN. Convolutional Layers Of CNN. Activation Function Of CNN. Pooling Layers Of CNN. Fully Connected (Dense) Layers. Output Layer Of CNN. Example CNN Architecture. (1) Architecture Diagram Of CNN (2) Input Layer Of CNN (3) Convolutional Layers Of CNN. (4) Activation Function (5) Pooling Layers Of CNN. (6) Fully Connected (Dense) Layers (7) Output Layer
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CNN – If We Are Flattening The Feature Map Aren’t We Loose The Spatial Information?
CNN – If We Are Flattening The Feature Map Aren’t We Loose The Spatial Information? Syllabus: What Happens During Flattening? Loss of Spatial Information Why Flattening is Used Despite This Limitation? Alternatives to Flattening Example Conclusion Answer: Yes, flattening a feature map in a Convolutional Neural Network (CNN) leads to the loss of spatial information because the structured 2D or 3D representation of the features is converted into a 1D vector. While this is acceptable for certain tasks (like classification), it does come with trade-offs. What Happens During Flattering? Loss Of Spatial Information: Why Flattening is Used Despite This Limitation:
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CNN – Why We Add Fully Connected Layer In CNN?
CNN – Why We Add Fully Connected layer In CNN? Syllabus: Why Fully Connected Layer Is Added? Why Fully Connected Layer Is Added To CNN Adding a fully connected (FC) layer in a Convolutional Neural Network (CNN) serves several critical purposes. It allows the network to perform classification or regression by combining the extracted features from previous layers into a final output. Here’s why the fully connected layer is important: Example Of Fully Connected Layer: Advantages Of Fully Connected Layer: Limitation Of Fully Connected Layer: Conclusion:
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CNN – Pooling Layer In CNN
CNN – Pooling Operation Syllabus: What Is Pooling In CNN ? Key Objectives Of Pooling. Types Of Pooling. Parameters Of Pooling. Example Of Pooling. Advantages Of Pooling. Disadvantages Of Pooling. Applications Of Pooling. Pooling Vs Convolution. Alternative To Pooling. Conclusion. (1) What Is Pooling In CNN ? Pooling is a critical operation in Convolutional Neural Networks (CNNs) that reduces the spatial dimensions of feature maps while retaining important information. It enhances computational efficiency and helps in achieving translational invariance. (2) Key Objectives Of Pooling (3) Types Of Pooling (4) Parameters Of Pooling (5) Mathematical Representation (6) Example Of Max Pooling
