TensorFlow: Simple Regression and Classification Models


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

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



Expected Duration (hours)
1.6

Lesson Objectives

TensorFlow: Simple Regression and Classification Models

  • 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
  • Course Number:
    it_sdaidt_02_enus

    Expertise Level
    Intermediate