Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools

Machine Learning    |    Intermediate
  • 12 Videos | 1h 3m 55s
  • Includes Assessment
  • Earns a Badge
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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

  • Playable
    1. 
    Course Overview
    55s
    UP NEXT
  • Playable
    2. 
    Comparing DL and ML
    6m 12s
  • Locked
    3. 
    ML/DL Workflow
    5m 59s
  • Locked
    4. 
    Deep Learning Network Components
    5m 14s
  • Locked
    5. 
    DL/ML Frameworks
    4m 26s
  • Locked
    6. 
    Recurrent CNN with Caffe2
    6m 12s
  • Locked
    7. 
    Autoencoders and PyTorch
    10m 47s
  • Locked
    8. 
    Deep Neural Network Implementation
    3m 36s
  • Locked
    9. 
    Platform and Framework Comparison
    3m 10s
  • Locked
    10. 
    Selecting the Right ML/DL Frameworks
    6m 19s
  • Locked
    11. 
    Challenges of Debugging Deep Learning Networks
    4m 41s
  • Locked
    12. 
    Exercise: Using DL Frameworks and Tools
    1m 24s

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