DL for Computer Vision with TensorFlow

The coursework provides an introductory overview of building deep learning models using TensorFlow for Computer Vision tasks.

Outline

These notebooks cover CNN model design from basic to advanced using TensorFlow. Topics that are covered in each notebook are as follows -

Basic CNN architecture design -- Dropout Regularization -- Batch Normalization
CNN with Callbacks
Using Pre-trained CNN architecture for prediction
Working on real-world datasets -- Data augmentation - how to prepare data in Kaggle
Transfer Learning
Hand-drawn Electric circuit Schematic Components prediction - how to prepare data in Google Colab
Practical example: Malaria parasite detection from blood smears using CNNs
Object Detection
U-Net

Name		Name	Last commit message	Last commit date
01. Creating CNN_architecture.ipynb		01. Creating CNN_architecture.ipynb
02. CNN with_Callbacks.ipynb		02. CNN with_Callbacks.ipynb
03_Using_Pre_trained_CNNs.ipynb		03_Using_Pre_trained_CNNs.ipynb
04. CNN on real world data with data augmentation.ipynb		04. CNN on real world data with data augmentation.ipynb
05. Transfer Learning.ipynb		05. Transfer Learning.ipynb
06. CNN Electric_circuits.ipynb		06. CNN Electric_circuits.ipynb
07. Malaria_parasite detection.ipynb		07. Malaria_parasite detection.ipynb
CNN_Padding.ipynb		CNN_Padding.ipynb
LICENSE		LICENSE
README.md		README.md
cnn_Padding.pdf		cnn_Padding.pdf