Multi-modal advanced deep learning architectures for breast cancer survival predictionBreast cancer prognosis predictionSigmoid gated attention convolutional neural network (SiGaAtCNN)Random forest (RF)Cross-modality attentionBi-AttentionUni-modal and multi-modal architecture...
whicharethebuildingblocksforthemoreadvancedtechniquesinthebook.You’lllearnhowtoimplementdeeplearningmodelswithKerasandTensorflow,andmoveforwardstoadvancedtechniques,asyouexploredeepneuralnetworkarchitectures,includingResNetandDenseNet,andhowtocreateAutoencoders.YouthenlearnallaboutGenerativeAdversarialNetworks(GANs),and...
processing each type of data using different kinds of neural layers. Imagine a deep-learning model trying to predict the most likely market price of a second-hand piece of clothing, using the following inputs: user-provided metadata (such as the item’s brand, age, and so ...
Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoenco...
learning techniques provide computational models of multiple processing layers to learn and represent data with multiple levels of abstraction. Deep Learning allows to implicitly capture intricate structures of large-scale data and ideally suited to some of the hardware architectures that are currently ...
You will start with simple, multi-layer dense networks (also known as multi-layer perceptrons), and continue on to more complicated architectures. The course will cover how to build models with multiple inputs and a single output, as well as how to share weights between layers in a model....
Cover advanced and state-of-the-art neural network architectures Understand the theory and math behind neural networks Train DNNs and apply them to modern deep learning problems Use CNNs for object detection and image segmentation Implement generative adversarial networks (GANs) and variational autoencod...
Furthermore, ResUNet++ significantly outperforms U-Net and ResUNet, two key state-of-the-art deep learning architectures, by achieving high evaluation scores with a dice coefficient of 81.33%, and a mean Intersection over Union (mIoU) of 79.27% for the Kvasir-SEG dataset and a dice ...
This study investigates the application of advanced deep learning architectures in classifying coffee varieties, addressing the complexities and demands of... El Jireh P. Bibangco,Mary Gift D. Dionson,Reign Kenneth T. Javier - 2024 IEEE Symposium on Wireless Technology & Applications (ISWTA) 被引...
Learn about the extended deep learning framework in MATLAB, which enables you to implement advanced network architectures such as generative adversarial networks (GANs), variational autoencoders (VAEs), or Siamese networks