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196       ¦¦❸ lIn-layer normalization techniques for training very deep neural networks 8 08-23
195       ¦¦❸ lBest Graph Neural Network architectures: GCN, GAT, MPNN and more 29 08-23
194       ¦¦❸ lHow Graph Neural Networks (GNN) work: introduction to graph convolutions from scratch 10 08-23
193       ¦¦❸ lGANs in computer vision - Improved training with Wasserstein distance, game theory control and progre... 7 08-23
192       ¦¦❸ lGANs in computer vision - Introduction to generative learning 16 08-23
191       ¦¦❸ lHow diffusion models work: the math from scratch 17 08-23
190       ¦¦❸ lTransformers in computer vision: ViT architectures, tips, tricks and improvements 4 08-23
189       ¦¦❸ lHow the Vision Transformer (ViT) works in 10 minutes: an image is worth 16x16 words 52 08-23
188       ¦¦❸ lHow Transformers work in deep learning and NLP: an intuitive introduction 16 08-23
187       ¦¦❸ lHow Attention works in Deep Learning: understanding the attention mechanism in sequence models 12 08-23
186       ¦¦❸ lThe theory behind Latent Variable Models: formulating a Variational Autoencoder 18 08-23
185       ¦¦❸ lHow to Generate Images using Autoencoders 21 08-23
184       ¦¦❸ lRecurrent neural networks: building a custom LSTM cell 13 08-23
183       ¦¦❸ lBest deep CNN architectures and their principles: from AlexNet to EfficientNet 15 08-23
182       ¦¦❸ lA journey into Optimization algorithms for Deep Neural Networks 17 08-23
181       ¦¦❸ lRegularization techniques for training deep neural networks 22 08-23
180    ¦¦❷ lSelf-attention-cv (various self-attention mechanisms focused on computer vision) 1 08-23
179    ¦¦❷ lMedicalZooPytorch (multi-modal 2D/3D medical image segmentation) 27 08-23
178    ¦¦❷ lAI Summer - Medical 20 08-23
177    ¦¦❷ lMedical Open Network for AI (MONAI) 5 08-23

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