Natural Language Processing
| Introduction –
· Courseoutline -Computer Vision.pdf
|
Working with Images
1. Working with Images_Introduction 2. Working with Images – Digitization, Sampling, and Quantization 3. Working with images – Filtering 4. Hands-on Python Demo: Working with images 5. Working with Images.ipynb |
| CNN Building Blocks
1. Introduction to Convolutions 2. 2D convolutions for Images 3. Convolution – Forward 4. Convolution – Backward 5. Transposed Convolution and Fully Connected Layer as a Convolution 6. Pooling : Max Pooling and Other pooling options 7. Hands-on Keras Demo: 1. MNIST CNN Building Blocks code walk-through 2. MNIST_CNN_Cloud.ipynb 3. MNIST_CNN_Colab.ipynb |
Project Work
1. Project Description.pdf 2. SVHN_CNN_Colab.ipynb 3. SVHN_CNN_Cloud.ipynb 4. SVHN_CNN_Colab_Solution.ipynb
|
| CNN Architectures, Transfer Learning, Visualizations
1. CNN Architectures and LeNet Case Study 2. Case Study : AlexNet 3. Case Study : ZFNet and VGGNet 4. Case Study : GoogleNet 5. Case Study : ResNet 6. Transfer Learning Principles and Practice 7. Hands-on Keras Demo: SVHN Transfer learning from MNIST dataset 1. SVHN_CNN_Transfer.ipynb 2. cnn_mnist_weights.h5 8. Visualization (run pacakge, occlusion experiment) 1. Hands on demo -T-SNE 2. t-SNE MNIST.ipynb
|
Semantic Segmentation and Object Detection
1. CNNs at Work – Semantic Segmentation 2. Semantic Segmentation process 3. U-Net Architecture for Semantic Segmentation 4. Hands-on demo – Semantic Segmentation using U-Net 5. UNet-StyleV1.ipynb 6. Other variants of Convolutions 7. Inception and Mobile Net models 8. CNNs at Work – Object Detection with region proposals 9. CNNs at Work – Object Detection with Yolo and SSD 10. Hands-on demo- Bounding box regressor 1. SingleBoundingBoxV1.ipynb 2. validation.csv 3. train.csv |
| Project Work
1. DLCP Project 2 Brief.pdf 2. Files Required for Face Detection 1. Files_required_for_face_detection.zip 3. FACE_DETECTION_Questions_final.ipynb 4. FACE_DETECTION_Solution.ipynb |
|
| CNNs at work: Siamese Network for Metric Learning
1. Metric Learning 2. Siamese Netwrok as metric learning 3. How to train a Neural Network in SIamese way 4. Hands-on demo – Siamese Network 1. Few Shot Learning – V1.ipynb 2. images_background.zip 3. images_background_small1.zip 4. images_background_small2.zip 5. images_evaluation.zip 6. train.pickle 7. val.pickle |
Project Work
1. DLCP Project 2 Brief.pdf 2. Files Required for Face recognition 1. new_FACE_RECOGNITION_Questions_updated.ipynb 2. new_FACE_RECOGNITION_Solution-1.ipynb 3. Siamese_SigNet_BHSig260.ipynb 4. Object_detection_fit.ipynb
|