In this course, you will learn how to create machine learning models in TensorFlow which is the tool we will use to write machine learning programs. You’ll learn how to use the TensorFlow libraries to solve numerical problems. When writing programs, you often want to know about common mistakes that you might run into, and how to fix common errors.
Then, we’ll look at the Estimator API, which provides the highest level abstraction within TensorFlow for training, evaluating and serving machine learning models. You will learn how to Use tf_estimator to create, train, and evaluate an ML model. Finally, you’ll learn how to execute TensorFlow models on Cloud ML Engine, Google-managed infrastructure to run TensorFlow. You will learn how to Train, deploy, and productionalize ML models at scale with Cloud Machine Learning Engine
Understand the key components of TensorFlow
Use the tf.data library to manipulate data and large datasets
Create machine learning models in TensorFlow
Use Keras sequential and functional APIs for model creation with Tensorflow 2.x
Train, deploy, and productionalize ML models at scale with Cloud AI Platform
-The tool we will use to write machine learning programs is TensorFlow and so in this course, we will introduce you to TensorFlow. In the first course, you learned how to formulate business problems as machine learning problems and in the second course, you learned how machine works in practice and how to create datasets that you can use for machine learning. Now that you have the data in place, you are ready to get started writing machine learning programs.
-We will introduce you to the core components of TensorFlow and you will get hands-on practice building machine learning programs. You will compare and write lazy evaluation and imperative programs, work with graphs, sessions, variables, as finally debug TensorFlow programs.
-In this module we will walk you through the Estimator API.
Scaling TensorFlow models
-I’m here to talk about how you would go about taking your TensorFlow model and training it on GCP’s managed infrastructure for machine learning model training and deployed.
-Here we summarize the TensorFlow topics we covered so far in this course. We'll revisit core TensorFlow code, the Estimator API, and end with scaling your machine learning models with Cloud Machine Learning Engine.