Getting Started with TensorFlow 2.0

TensorFlow has long been a powerful and widely-used framework for building and training neural network models. TensorFlow 2.0's use of the Keras high-level API makes designing and training neural networks straightforward, while its eager execution mode makes prototyping and debugging models simple. This course first explores the basic features of TensorFlow 2.0 and how its programming model differs from TensorFlow 1.x. It covers the basic working of a neural network and its active learning unit, the neuron. It then compares and contrasts static and dynamic computation graphs and explains the advantages and disadvantages of each. It explains how a neural network is trained using gradient descent optimization, and how the GradientTape library in TensorFlow calculates gradients automatically during training. Finally, it works with the different APIs in Keras and shows how they lend themselves to different use cases — sequential models (layers stacked one on top of another), the functional API, and model subclassing — used to build regression and classification models.

This material assumes a basic understanding of machine learning algorithms and some prior experience building and tr...