Blog category: cs231n

• Archive: First steps with Convolutional Networks

  Implementing convolutional neural networks forwards and backwards passes with Numpy, and convolution weights visualization/interpretation.

• Archive: Dropout and batch normalization

  Implementing dropout and batch norm to make training models easier and improve their evaluation performance.

• Archive: More with fully-connected nets

  Expanding the PyTorch-inspired neural net programming interface to accept arbitrary model shapes and additional training optimization schemes.

• Archive: Feature extraction and fully-connected neural networks

  First steps with feature extraction, followed by making a reusable neural network interface which loosely mirrors PyTorch's design.

• Archive: Two layer neural net

  How I made my first neural network to perform image classification on CIFAR-10.

• Archive: Softmax classifier

  How I derived and developed a linear softmax classifier on the CIFAR-10 dataset.

• Archive: Implementing KNN and Support Vector Machine classifiers

  The first post in the deep learning series, this marks the beginning of my journey to get up to speed with the latest computer vision literature.