Projects done in a Deep Learning Lab course
Table of Contents
- Week3: Overview of Convolutional Neural Networks; VGGNet and ResNet
- Week4: Limitation - Spatial Transformer Networks (STN)
- Week5: Accelerating the Super-Resolution Convolutional Neural Network - FSRCNN
- Week6: Overview of Semantic Segmentation - FCN and DilatedNet
- Week7: You Only Look Once - Unified, Real-Time Object Detection
- Week9: Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
- Week10: Image Style Transfer Using Convolutional Neural Network and Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- Week11: Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
- Week12: Overview of Sequence to Sequence Learning with Neural Networks (Seq2Seq) and Empirical Evaluation of Gated Recurrent Neural Networks (GRU) on Sequence Modeling
- Week13: Overview of Generative Adversarial Nets (GAN)
- Week14: Overview of Conditional Generative Adversarial Nets and Image-to-Image Translation with Conditional Adversarial Networks