- Use of PyTorch
- Implemented Gradient Descent
- Designing NN with Achitecture and Activation functions
- Training NN with different techniques like regularisation, early stopping, dropout, ranmdom restart, etc.
- Used MLP to classify MINST data with accuracy 97%
- Convolution Layer Visulization, Pooling Layer Visualization, CNNs for CIFAR Image Classification, Improving Performance using BatchgNorm and Image Augmentation and Exporting for Production
- Transfer Learning
- Linear Autoencoders, Convolutional Autoencoders, Denoising Autoencoders
- Object Detection using RetinaNet, Semantic Segmentation using UNet
- Created a CNN to classify landmarks from scratch.
- Used Transfer Learning with resnet18 to classify same landmarks.
- Deployed an app
- MNIST GAN
- DCGAN Generator Discriminator, Training, Frechet Inception Distance
- CycleGAN
- ProGAN, StyleGAN
- Build a custom generative adversarial network to generate new images of faces.
- Used a dataset of high-resolution images of "celebrity" faces.