• Online, Self-Paced
Course Description

Explore how to how to build and train the two most versatile and ubiquitous types of deep learning models in TensorFlow.

Learning Objectives

TensorFlow: Simple Regression and Classification Models

  • Course Overview
  • recognize linear regression problems and extend that to general machine learning problems
  • recognize how model parameter training happens via gradient descent to find minimum loss
  • load a dataset and explore its features and labels
  • choose the right form of data to feed into the linear regression model
  • build a base model for comparison with scikit-learn
  • create placeholders, training variables, and instantiate optimizers to use with regression
  • train model parameters using a session and the training dataset, and visualize the result with Matplotlib
  • demonstrate how to interpret the loss and summaries on TensorBoard
  • choose the high-level Estimator API for common use cases
  • train a regression model using the high-level Estimator API
  • evaluate and predict housing prices using estimators
  • identify classification problems and recall logistic regression for classification
  • recognize cross entropy as the loss function for classification problems and use softmax for n-category classification
  • identify data as being a continuous range or comprised of categorical values
  • work with training and test data to predict heart disease
  • train the high-level estimator for classification and use it for prediction
  • describe basic concepts of the linear regression machine learning model
  • describe basic concepts of the binary classification machine learning model

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Software Development