Machine-learning

The goal of this guide is to teach someone who knows the basic of machine learning how to train a nearly state of the art image classifier from scratch.

The goal of this guide is to teach someone who knows the basic of machine learning how to train an extremely powerful object detection model from scratch. This guide explains a wide variety of techniques and gives some theoretical backing as to why they are effective.

Approximate non one to one mapping using mixture density networks.

Construct a powerful object detection pipeline to detect marine animals

Learn about the tradeoffs of reducing losses using `sum()`, and reducing losses using `mean()`.

Overview of a common pitfall Keras users hit when writing subclass models

Discussion about why the Elixir programming language sticks out.

Deep dive into the internals of the Keras model class by writing a custom training loop duplicating the behavior of `keras.Model`.

Use Parametric Spectral Filters to construct an autoencoder.

TensorFlow has limited complex number support. Write the operations yourself to maintain automatic differentiation.