osamaa-mustafa/Image_segmentation_Unet_v2-1-Image_segmentation_Unet_v2-1- osamaa-mustafa/Image_segmentation_Unet_v2-1-Image_segmentation_Unet_v2-1-

  • Stars
    star
    1
  • Language
    Jupyter Notebook
  • License
    MIT License
  • Created almost 3 years ago
  • Updated almost 3 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
osamaa-mustafa/Image_segmentation_Unet_v2-1-Image_segmentation_Unet_v2-1-
github

osamaa-mustafa

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

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.

More Repositories

1

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
Jupyter Notebook
2
star
2

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.
Jupyter Notebook
1
star
3

DETR-Object-Detection-in-Dashcam-Images

Jupyter Notebook
1
star
4

Residual_Networks

Residual_Networks
Jupyter Notebook
1
star
5

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!
Jupyter Notebook
1
star