DenseNet | Densely Connected Convolutional Networks скачать в хорошем качестве

DenseNet | Densely Connected Convolutional Networks

Learn DenseNet – Dense Convolutional Network for Image Classification in this detailed tutorial. DenseNet overcomes the vanishing gradient problem and provides higher accuracy compared to other deep convolutional neural networks by connecting every layer directly to all subsequent layers. 📌 Topics Covered: What is DenseNet? Architecture of DenseNet with Dense Blocks and Transition Layers How DenseNet strengthens feature propagation and encourages feature reuse Advantages of DenseNet over other CNNs: Reduces the number of parameters Improves gradient flow Enhances feature reuse for efficient learning DenseNet Architecture: Each Dense Block connects all layers directly using concatenation of feature maps. Transition Layers with 1×1 Conv + 2×2 Avg Pooling are used to maintain manageable feature map sizes. Each convolution layer includes BatchNorm → ReLU → 3×3 Conv (optionally Dropout). Learn how DenseNet improves image classification tasks and why it is preferred over traditional CNN architectures. For queries or collaborations: aarohisingla1987@gmail.com #DenseNet #CNN #ConvolutionalNeuralNetwork #ImageClassification #DeepLearning #ArtificialIntelligence #AI #ComputerVision #DenseBlocks #TransitionLayer #FeaturePropagation #FeatureReuse #VanishingGradient #PifordTechnologies