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This video is very useful for learn Deep learning Lecture by Nagarajan.S Assistant Professor CINTEL SRMIST R2U-Net is an image segmentation task-focused network that mixes residual and recurrent connections to boost performance and manage sequential data. Encoder for U-Net: The R2U-Net starts with a typical U-Net encoder, which is made up of multiple convolutional blocks that are organized in a contracting route. Each block is often composed of convolutional layers, activation functions (such as ReLU), and down-sampling processes (such as max-pooling) While extracting hierarchical features, the encoder gradually lowers the spatial dimensions of the input image. Residual Connections: Residual connections are introduced within the U-Net blocks in the R2U-Net. These links allow the network to learn the residual (or difference) between a block’s output and its input . The residual is then added back to the original input, assisting in gradient propagation during training and allowing the network to acquire richer representations without vanishing gradients. Recurring Connections: The addition of recurring connections is the R2U-Net’s fundamental innovation. Each U-Net block (both encoder and decoder) includes a recurrent layer, such as a Gated Recurrent Unit (GRU) . By keeping an internal memory state that changes and transfers information through time, these recurrent connections allow the network to capture long-term dependencies in the data. U-Net Decoder: Following the encoder, the R2U-Net includes a U-Net decoder, which is made up of multiple convolutional blocks arranged in a long path. Each block is typically composed of transposed convolutions (also called up-sampling or deconvolution), concatenation with encoder skip connections, and convolutional layers with activation functions. #Tutorial for Machine Learning • #Lec 2: Machine Learning Life cycle|#Tutor... #Tutorial for DeepLearning #Tutorial for DBMS(GATE/UGC/NET/ALL UNIV) • #Tutorial for Operating System #Tutorial for Computer Networks(GATE/UGC/NET) • #Tutorial for Computer networks #Tutorial for Java Beginners(GATE/UGC/NET) • #Tutorial for Java Beginners #Tutorial for C programming(GATE/UGC/NET) • #Tutorial for C Programming #Tutorial for Artificial Intelligence(GATE/UGC/NET) • #Tutorial for Artificial Intelligence #Tutorial for Computer Organization/Architecture(GATE/UGC/NET) • computer organization/architecture GATE a... #Tutorial for Python Beginners • Tutorial for Python Beginners #Tutorial for Python Interview Q& A by Mr.Uday • #tutorial for Python Interview Q& A by Mr.... #Tutorial for Computer Organization(GATE/UGC/NET) • Computer Organization(JNTUA R-15,R-19 All ... #Carrier Tips For B.tech Freshers • #Careertips for B.Tech freshers( part-... Thanks For Watching. You can follow and Like us on following social media. facebook: nagarajanhostboxcse LinkedIn-nag1984rajan@gmail.com Skip connections, like the original U-Net, are used to connect the relevant encoder and decoder blocks . By using these skip connections, the decoder can produce feature maps with high resolution from the encoder, assisting in the recovery of spatial in formation and the improvement of segmentation accuracy . #Tutorial for Machine Learning • #Lec 2: Machine Learning Life cycle|#Tutor... #Tutorial for DeepLearning #Tutorial for DBMS(GATE/UGC/NET/ALL UNIV) • #Tutorial for Operating System #Tutorial for Computer Networks(GATE/UGC/NET) • #Tutorial for Computer networks #Tutorial for Java Beginners(GATE/UGC/NET) • #Tutorial for Java Beginners #Tutorial for C programming(GATE/UGC/NET) • #Tutorial for C Programming #Tutorial for Artificial Intelligence(GATE/UGC/NET) • #Tutorial for Artificial Intelligence #Tutorial for Computer Organization/Architecture(GATE/UGC/NET) • computer organization/architecture GATE a... #Tutorial for Python Beginners • Tutorial for Python Beginners #Tutorial for Python Interview Q& A by Mr.Uday • #tutorial for Python Interview Q& A by Mr.... #Tutorial for Computer Organization(GATE/UGC/NET) • Computer Organization(JNTUA R-15,R-19 All ... #Carrier Tips For B.tech Freshers • #Careertips for B.Tech freshers( part-... Thanks For Watching. You can follow and Like us on following social media. facebook: nagarajanhostboxcse LinkedIn-nag1984rajan@gmail.com