TensorFlow: Simple Regression & Classification Models

TensorFlow    |    Intermediate
  • 19 videos | 1h 36m 57s
  • Includes Assessment
  • Earns a Badge
Rating 4.2 of 19 users Rating 4.2 of 19 users (19)
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

  • 1m 52s
  • 8m 27s
    Upon completion of this video, you will be able to recognize linear regression problems and extend your knowledge to general machine learning problems. FREE ACCESS
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    3.  Gradient Descent and Optimizers
    4m 42s
    After completing this video, you will be able to recognize how model parameter training happens via gradient descent to find the minimum loss. FREE ACCESS
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    4.  Explore the Boston Housing Prices Dataset
    5m 15s
    Find out how to load a dataset and explore its features and labels. FREE ACCESS
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    5.  Creating Training and Test Datasets for Regression
    8m 13s
    In this video, learn how to choose the right form of data to use for the linear regression model. FREE ACCESS
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    6.  Base Model with scikit-learn
    3m 19s
    Find out how to build a base model using scikit-learn. FREE ACCESS
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    7.  Setting up the Linear Regression Computation Graph
    7m 21s
    In this video, you will learn how to create placeholders, training variables, and instantiate optimizers to use with regression. FREE ACCESS
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    8.  Train and Visualize the Linear Regression Model
    7m 26s
    Learn how to train model parameters using a session and the training dataset, and visualize the results with Matplotlib. FREE ACCESS
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    9.  Visualize the Model with TensorBoard
    2m 56s
    In this video, you will learn how to interpret the loss and summaries on TensorBoard. FREE ACCESS
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    10.  The High-Level Estimator API
    2m 16s
    In this video, find out how to choose the high-level Estimator API for common use cases. FREE ACCESS
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    11.  Linear Regression with Estimators
    8m 45s
    Learn how to train a regression model using the Estimator API. FREE ACCESS
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    12.  Prediction Using Estimators
    4m 12s
    In this video, you will evaluate and predict housing prices using estimators. FREE ACCESS
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    13.  Understanding Binary Classification
    3m 58s
    In this video, you will learn how to identify classification problems and recall logistic regression for classification. FREE ACCESS
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    14.  The Cross Entropy Loss Function and Softmax
    4m 13s
    Upon completion of this video, you will be able to recognize cross entropy as the loss function for classification problems and use softmax for n-category classification. FREE ACCESS
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    15.  Continuous and Categorical Data
    2m 9s
    Learn how to identify data as being a continuous range or as categorical values. FREE ACCESS
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    16.  Creating Training & Test Datasets for Classification
    8m 25s
    Learn how to work with training and test data to predict heart disease. FREE ACCESS
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    17.  Binary Classification Using Estimators
    4m 42s
    In this video, you will train the high-level estimator for classification and use it for prediction. FREE ACCESS
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    18.  Exercise: Working with Linear Regression
    4m 3s
    Upon completion of this video, you will be able to describe basic concepts of the linear regression machine learning model. FREE ACCESS
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    19.  Exercise: Working with Binary Classification
    4m 45s
    Upon completion of this video, you will be able to describe basic concepts of the binary classification machine learning model. FREE ACCESS

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