osamaa-mustafa/DETR-Object-Detection-in-Dashcam-Images

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 Answering

Convolution_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.

Residual_Networks

Residual_Networks

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!