Intro to TensorFlow
This course is focused on using the flexibility and “ease of use” of TensorFlow 2.x and Keras to build, train, and deploy machine learning models. We will learn about the TensorFlow 2.x API hierarchy and will get to know the main components of TensorFlow through hands-on exercises. We will describe how to work with datasets and feature columns. We will learn how to design and build a TensorFlow 2.x input data pipeline. We will get hands-on practice loading csv data, numPy arrays, text data, and images using tf.Data.Dataset. We will also get hands-on practice creating numeric, categorical, bucketized, and hashed feature columns.
The Keras Sequential API and the Keras Functional API is used to create deep learning models. We’ll talk about activation functions, loss, and optimization. Our Jupyter Notebooks hands-on labs offer the opportunity to build basic linear regression, basic logistic regression, and advanced logistic regression machine learning models. We will learn how to train, deploy, and productionalize machine learning models at scale with Cloud AI Platform.