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01:17:50 Advanced Machine Learning 2022 - day 23… Advanced Machine Learning 2022 - day 23 Convolutional Autoencoders	Advanced Machine Learning 2022 - day 23 Convolutional Autoencoders Convolutional Autoencoders image channels things convolution dimensionality images representation class input columns rows thing max kinds point layers layer pixels kind pixel From Andrew Fagg 0 likes 6 plays 0
01:20:46 Advanced Machine Learning 2022 - day 12… Advanced Machine Learning 2022 - day 12 Convolutional Neural Networks	Advanced Machine Learning 2022 - day 12 Convolutional Neural Networks Convolutional neural networks: first code, 2D convolution; Dropout; First image classification architecture layer class kernel things input questions image bit images training point network dataset validation parameters size model padding channels thing From Andrew Fagg 0 likes 5 plays 0
01:15:08 Advanced Machine Learning - Day 27 Advanced Machine Learning - Day 27	Advanced Machine Learning - Day 27 HW 7; Generative Adversarial Networks vchr3051 images training image convolution discriminator generator step parameters pathway channels memory lot validation bit process model sense operation steps filters From Andrew Fagg 0 likes 23 plays 0
01:20:17 Advanced Machine Learning - Day 26 Advanced Machine Learning - Day 26	Advanced Machine Learning - Day 26 Convolutional Autoencoders in practice vchr3051 channels class pixel network images training output layer bit things channel layers tensor convolution classes input files size image lot From Andrew Fagg 0 likes 40 plays 0
01:17:07 Advanced Machine Learning - Day 15 Advanced Machine Learning - Day 15	Advanced Machine Learning - Day 15 HW 4 (CNNs); Keras Model API (building bigger models) vchr3051 layer output input tensor network model things training inception channels module gradient convolution generator step lot images question function bit From Andrew Fagg 0 likes 56 plays 0
01:04:09 Advanced Machine Learning - Day 13 Advanced Machine Learning - Day 13	Advanced Machine Learning - Day 13 Convolutional Neural Networks: convolution and pooling layers; building an image recognizer vchr3051 image layer filter parameters pixels input filters dimensionality convolution channels stride kinds cell layers network edges output rows size access From Andrew Fagg 0 likes 24 plays 0