Practical deep learning

A short written introduction to tensors, training, and evaluation. Video recordings and slides can be added to each lesson when available.

  1. Tensors and shapes

    A tensor stores values in a multidimensional array. In image learning, dimensions often describe the batch, channels, height, and width.

  2. Training and evaluation

    Training adjusts model parameters using examples and a loss function. Validation informs choices such as hyperparameters and stopping time. The test set evaluates the final procedure after those choices are fixed.

  3. Working with RGB-D observations

    RGB describes appearance. Depth describes distance according to the sensor's coordinate system and unit convention. Combining them requires attention to calibration, alignment, missing measurements, and scale.