Course details

Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools

Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

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.



Expected Duration (hours)
1.0

Lesson Objectives

Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools

  • 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
  • Course Number:
    it_mlbrmddj_01_enus

    Expertise Level
    Intermediate