Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools

Machine Learning    |    Intermediate
  • 12 videos | 58m 55s
  • Includes Assessment
  • Earns a Badge
Rating 4.1 of 17 users Rating 4.1 of 17 users (17)
Explore the concept of deep learning, including a comparison between machine learning and deep learning (ML/DL) in this 12-video course. Learners will examine the various phases of ML/DL workflows involved in building deep learning networks; recall the essential components of building and applying deep learning networks; and take a look at the prominent frameworks that can be used to simplify building ML/DL applications. You will then observe how to use the Caffe2 framework for implementing recurrent convolutional neural networks; write PyTorch code to generate images using autoencoders; and implement deep neural networks by using Python and Keras. Next, compare the prominent platforms and frameworks that can be used to simplify deep learning implementations; identify and select the best fit frameworks for prominent ML/DL use cases; and learn how to recognize challenges and strategies associated with debugging deep learning networks and algorithms. The closing exercise involves identifying the steps of ML workflow, deep learning frameworks, and strategies for debugging deep learning networks.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Define the concept of deep learning and compare the differences between machine learning and deep learning
    Describe the various phases of ml/dl workflows involved in building deep learning networks
    Recall the essential components of building and applying deep learning networks
    List the prominent frameworks that can be used to simplify building ml/dl applications
    Use the caffe2 framework to build recurrent convolution neural networks
  • Write pytorch code to generate images using autoencoders
    Implement deep neural networks using python and keras
    Compare the prominent platforms and frameworks that can be used to simplify deep learning implementations
    Identify and select the best fit frameworks for prominent ml/dl use cases
    Recognize the challenges and strategies associated with debugging deep learning networks and algorithms
    Identify steps of machine learning workflow, deep learning frameworks, and strategies for debugging deep learning networks

IN THIS COURSE

  • 55s
  • 6m 12s
    In this video, you will learn how to define the concept of deep learning and compare the differences between machine learning and deep learning. FREE ACCESS
  • Locked
    3.  ML/DL Workflow
    5m 59s
    After completing this video, you will be able to describe the various phases of ML/DL workflows involved in building deep learning networks. FREE ACCESS
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    4.  Deep Learning Network Components
    5m 14s
    After completing this video, you will be able to recall the essential components of building and applying deep learning networks. FREE ACCESS
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    5.  DL/ML Frameworks
    4m 26s
    Upon completion of this video, you will be able to list the prominent frameworks that can simplify building ML/DL applications. FREE ACCESS
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    6.  Recurrent CNN with Caffe2
    6m 12s
    In this video, learn how to use the Caffe2 framework to build recurrent convolutional neural networks. FREE ACCESS
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    7.  Autoencoders and PyTorch
    10m 47s
    During this video, you will learn how to write PyTorch code to generate images using autoencoders. FREE ACCESS
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    8.  Deep Neural Network Implementation
    3m 36s
    In this video, you will learn how to implement deep neural networks using Python and the Keras library. FREE ACCESS
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    9.  Platform and Framework Comparison
    3m 10s
    In this video, find out how to compare the prominent platforms and frameworks that can simplify deep learning implementations. FREE ACCESS
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    10.  Selecting the Right ML/DL Frameworks
    6m 19s
    During this video, you will learn how to identify and select the best-fitting frameworks for prominent ML/DL use cases. FREE ACCESS
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    11.  Challenges of Debugging Deep Learning Networks
    4m 41s
    Upon completion of this video, you will be able to recognize the challenges and strategies associated with debugging deep learning networks and algorithms. FREE ACCESS
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    12.  Exercise: Using DL Frameworks and Tools
    1m 24s
    During this video, you will learn how to identify the steps of a machine learning workflow, deep learning frameworks, and strategies for debugging deep learning networks. FREE ACCESS

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