kjy/MachineLearning_TensorFlow_GoogleCloudPlatform_course3_IntroToTensorFlow

Writing low-level TensorFlow programs. Learned how TensorFlow Python API works by building a graph, running a graph, and feeding values into a graph. Calculated area of a triangle using TensorFlow. Implementing a Machine Learning model in TensorFlow using Estimator API. Implemented a simple machine learning model using tf.learn. Read csv data into a Pandas dataframe. Implemented a linear regression model in TensorFlow. Trained and evaluated the model. Predicted with the model. Repeated with a Deep Neural Network (DNN) model in TensorFlow. Scaling up TensorFlow ingest using batching. Loaded large dataset progressively using tf.data.Dataset. Broke the one-to-one relationship between inputs and features. Creating a distributed training TensorFlow model with Estimator API. Learned the importance of watching your validation metrics while training is in progress. Used the estimator.train_and_evaluate function. Monitored training using TensorBoard. Scaling TensorFlow with Cloud Machine Learning Engine. Packaged up TensorFlow model. Ran training locally. Ran training on cloud. Deployed model to cloud. Invoked model to carry out predictions.