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C5_W4_A1_Transformer_Subclass_v1-1-
C5_W4_A1_Transformer_Subclass_v1 (1). Learning Objectives Create positional encodings to capture sequential relationships in data Calculate scaled dot-product self-attention with word embeddings Implement masked multi-head attention Build and train a Transformer model Fine-tune a pre-trained transformer model for Named Entity Recognition Fine-tune a pre-trained transformer model for Question Answering Implement a QA model in TensorFlow and PyTorch Fine-tune a pre-trained transformer model to a custom dataset Perform extractive Question AnsweringConvolution_model_Step_by_Step_v1
Implement the foundational layers of CNNs (pooling, convolutions) and stack them properly in a deep network to solve multi-class image classification problems.Image_segmentation_Unet_v2-1-Image_segmentation_Unet_v2-1-
Image_segmentation_Unet_v2 (1). Apply your new knowledge of CNNs to one of the hottest (and most challenging!) fields in computer vision: object detection.DETR-Object-Detection-in-Dashcam-Images
Art_Generation_with_Neural_Style_Transfer
Art_Generation_with_Neural_Style_Transfer. Explore how CNNs can be applied to multiple fields, including art generation and face recognition, then implement your own algorithm to generate art and recognize faces!Love Open Source and this site? Check out how you can help us