• Introduction
  • Basics
    • Optimization
    • How to prevent overfitting
    • Linear Algebra
    • Clustering
    • Calculate Parameters in CNN
    • Weight norm and layer norm
    • Confidence Interval
    • Quantization
  • Classical Machine Learning
  • Neural Networks
    • Different Types of Convolution
    • Loss
      • Hinge Loss
      • Cross-Entropy Loss
      • Binary Cross-Entropy Loss
      • Categorical Cross-Entropy Loss
      • Optional: Focal Loss
      • Optional: CORAL Loss
    • Resnet
    • Mobilenet
  • Computer Vision
    • Two Stage Object Detection
      • Metrics
      • ROI
      • R-CNN
      • Fast RCNN
      • Faster RCNN
      • Mask RCNN
    • One Stage Object Detection
      • YOLO
      • Single Shot MultiBox Detector(SSD)
      • FPN
    • Segmentation
      • Panoptic Segmentation
      • PSPNet
    • FaceNet
    • GAN
    • Imbalance problem in object detection
  • NLP
    • Embedding
    • RNN
    • LSTM
  • Parallel Computeing
    • Communication
    • MapReduce
    • Parameter Server
    • Decentralized And Ring All Reduce
    • Federated Learning
  • Anomaly Detection
    • DBSCAN
    • Autoencoder
  • Visualization
    • Saliency Maps
    • Fooling images
    • Class Visualization
  • Published with GitBook

NLP