TensorFlow: Introduction to Machine Learning

TensorFlow    |    Intermediate
  • 19 videos | 1h 40m 26s
  • Includes Assessment
  • Earns a Badge
Explore the concept of machine learning in TensorFlow, including TensorFlow installation and configuration, the use of the TensorFlow computation graph, and working with building blocks.

WHAT YOU WILL LEARN

  • Describe kinds of machine learning algorithms and their use cases
    Define the training and prediction phases in machine learning
    Define the conceptual differences between traditional machine learning and deep learning
    Compare and contrast supervised and unsupervised techniques in machine learning
    Define the advantages and challenges in using tensorflow for machine learning
    Distinguish data and computations as distinct building blocks of a tensorflow computation graph
    Choose the right way to install tensorflow based on the user's environment
    Install tensorflow and work with jupyter notebooks
    Specify constants and build and run a computation graph
  • Use tensorboard to visualize the computation graph
    Build and execute a computation graph with variables and placeholders
    Visualize variables and placeholders on tensorboard
    Recognize how variables are trainable parameters and can be updated within a session
    Work with feed dictionaries to input data to placeholders during training
    Use named scopes to group computations
    Specify and work with eager execution for prototyping and development
    Recall basic concepts of machine learning and tensorflow
    Build and execute computation graphs with computation nodes and data

IN THIS COURSE

  • 2m 9s
  • 8m 21s
    Upon completion of this video, you will be able to describe different kinds of machine learning algorithms and their use cases. FREE ACCESS
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    3.  Understanding Machine Learning
    8m 47s
    In this video, you will learn about the training and prediction phases in machine learning. FREE ACCESS
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    4.  Understanding Deep Learning
    3m 42s
    In this video, you will learn about the conceptual differences between traditional machine learning and deep learning. FREE ACCESS
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    5.  Supervised and Unsupervised Learning
    4m 25s
    Learn how to compare and contrast supervised and unsupervised techniques in machine learning. FREE ACCESS
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    6.  TensorFlow for Machine Learning
    4m 52s
    In this video, you will learn how to define the advantages and challenges of using TensorFlow for machine learning. FREE ACCESS
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    7.  Tensors and Operators
    7m 48s
    Learn how to distinguish data and computations as distinct building blocks of a TensorFlow computation graph. FREE ACCESS
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    8.  Understanding How to Install TensorFlow
    5m 42s
    In this video, you will learn how to choose the right way to install TensorFlow based on the user's environment. FREE ACCESS
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    9.  Installing TensorFlow on the Local Machine
    3m 57s
    In this video, you will install TensorFlow and work with Jupyter Notebooks. FREE ACCESS
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    10.  Working with Constants
    8m 51s
    After completing this video, you will be able to specify constants and build and run a computation graph. FREE ACCESS
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    11.  The Computation Graph with TensorBoard
    2m 37s
    In this video, you will use TensorBoard to visualize the computation graph. FREE ACCESS
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    12.  Working with Variables and Placeholders
    8m 1s
    In this video, you will build and execute a computation graph with variables and placeholders. FREE ACCESS
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    13.  Variables and Placeholders on TensorBoard
    2m 45s
    During this video, you will learn how to visualize variables and placeholders using TensorBoard. FREE ACCESS
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    14.  Updating Variables in a Session
    4m 40s
    After completing this video, you will be able to recognize how variables are trainable parameters and can be updated within a session. FREE ACCESS
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    15.  Feed Dictionaries
    7m 46s
    In this video, you will learn how to work with feed dictionaries to input data to placeholders during training. FREE ACCESS
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    16.  Named Scopes for Better Visualization
    2m 25s
    Find out how to use named scopes to group together computations. FREE ACCESS
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    17.  Eager Execution
    4m 46s
    After completing this video, you will be able to specify and work with eager execution for prototyping and development. FREE ACCESS
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    18.  Exercise: Machine Learning and TensorFlow
    5m 9s
    Upon completion of this video, you will be able to recall basic concepts of machine learning and TensorFlow. FREE ACCESS
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    19.  Exercise: Working with Computation Graph
    3m 45s
    Find out how to build and execute computation graphs with computation nodes and data. FREE ACCESS

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