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      • Generative Modelling
    • Making GANs train
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      • Chapter 3: Finite Markov Decision Processes
      • Chapter 4: Dynamic Programming
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    • ImageGen
  • Distributed Training
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  • RLHF
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    • Kalman Filter
    • Unscented Kalman Filter
  • Game Theory and ML
    • 1st Lecture - 19/01
    • Lecture 2 - 22/01
    • Lecture 4: Optimization
  • Continual Learning
    • Lecture - 21/01
    • iCaRL: Incremental Classifier and Representation Learning
    • Variational Continual Learning
  • Computer Vision
    • Hough Transform
    • Projective Geometry
      • Extrinsic and Intrinsic Parameters
      • Image Rectification
    • Tracking
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    • Harris Corner
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  • Papers
    • To Be Read
    • Probabilistic Object Detection and Uncertainty Estimation
      • BayesOD
      • Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection
      • Gaussian YOLOv3
      • Dropout Sampling for Robust Object Detection in Open-Set Condition
      • *Sampling Free Epistemic Uncertainty Estimation using Approximated Variance Propagation
      • Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
      • Can We Trust You? On Calibration of Probabilistic Object Detector for Autonomous Driving
    • Object Detection
    • Temporal Fusion in Object Detection/ Video Object Detection
    • An intriguing failing of convolutional neural networks and the CoordConv solution
    • A Neural Algorithm of Artistic Style - A.Gatys
  • Deep Learning Book
    • Chapter 4: Optimization
    • Chapter 5: Machine Learning Basics
    • Chapter 6: Deep FeedForward Networks
  • Python
    • Decorators
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      • Pip
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  • TensorFlow
  • Pytorch
    • RNN/LSTM in Pytorch
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  • Programming
    • Unit Testing
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  • General Software Engineering
    • SSH tunneling and Ngrok
  • How To Do Research
  • Resources
  • ROS for python3
  • Kitti
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Distributed Training

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