TensorFlow: Simple Regression & Classification Models

TensorFlow    |    Intermediate
  • 19 Videos | 1h 44m 57s
  • Includes Assessment
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Explore how to how to build and train the two most versatile and ubiquitous types of deep learning models in TensorFlow.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 52s
    UP NEXT
  • Playable
    2. 
    Understanding Linear Regression
    8m 27s
  • Locked
    3. 
    Gradient Descent and Optimizers
    4m 42s
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    4. 
    Explore the Boston Housing Prices Dataset
    5m 15s
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    5. 
    Creating Training and Test Datasets for Regression
    8m 13s
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    6. 
    Base Model with scikit-learn
    3m 19s
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    7. 
    Setting up the Linear Regression Computation Graph
    7m 21s
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    8. 
    Train and Visualize the Linear Regression Model
    7m 26s
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    9. 
    Visualize the Model with TensorBoard
    2m 56s
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    10. 
    The High-Level Estimator API
    2m 16s
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    11. 
    Linear Regression with Estimators
    8m 45s
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    12. 
    Prediction Using Estimators
    4m 12s
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    13. 
    Understanding Binary Classification
    3m 58s
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    14. 
    The Cross Entropy Loss Function and Softmax
    4m 13s
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    15. 
    Continuous and Categorical Data
    2m 9s
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    16. 
    Creating Training & Test Datasets for Classification
    8m 25s
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    17. 
    Binary Classification Using Estimators
    4m 42s