TF. Learn is a high-level Python module for distributed machine learning inside TensorFlow.
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production.
Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production.
This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion.
NAMs learn a linear combination of neural networks that each attend to a single input feature.
AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process.
This new formulation leads to an algorithm that produces a stochastic classifier by playing a two-player non-zero-sum game solving for what we call a semi-coarse correlated equilibrium, which in turn corresponds to an approximately optimal and feasible solution to the constrained optimization problem.