Machine Learning with PyTorch LiveLessons (Video Training) by Pearson Education (Inform IT) | Coursetakers.com

Machine Learning with PyTorch LiveLessons (Video Training)

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Learn the main concepts and techniques used in modern machine learning and deep neural networks through numerous examples written in PyTorch.

$239
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6+ Hours

Course Details

Online Machine Learning with PyTorch LiveLessons (Video Training) course by InformIT

 

Overview:

This course begins with the basic concepts of machine and deep learning. Subsequently, you gain a reasonable familiarity with the main features of PyTorch and learn how it can be applied to some popular problem domains.

 

Learn How To:

  • Apply various machine and deep learning techniques
  • Understand the difference between various machine and deep learning libraries
  • Create classifiers
  • Enhance an existing classifier

 

Course Requirements:

Programming experience

 

Lessons:

Lesson 1: What Is Machine Learning? What Is Deep Learning?

Learning objectives:

1.1 Understand the course at a high level

1.2 Describe the techniques used in machine learning

1.3 Describe the libraries used in machine learning

1.4 Understand the difference between “deep learning” and other ML techniques

1.5 Utilize additional concepts in ML

1.6 Understand the types of network layers and activation functions

1.7 Understand metrics

 

Lesson 2: Comparing Several Libraries

Learning objectives:

2.1 Perform a task in scikit-learn

2.2 Perform a task in Keras (with TensorFlow)

2.3 Perform a task in PyTorch

2.4 Classify an image with PyTorch

 

Lesson 3: Understanding PyTorch

Learning objectives:

3.1 Use tensors, autograd, and NumPy interfaces

3.2 Establish a low-level neural network

3.3 Implement a neural network with torch.nn

3.4 Understand why bias is important

3.5 Identify other torch tools

 

Lesson 4: Tasks with Networks

Learning objectives:

4.1 Create a simple feature classifier—Part 1

4.2 Create a simple feature classifier—Part 2

4.3 Create an image classifier

4.4 Utilize regression prediction

4.5 Do clustering with PyTorch

4.6 Use generative adversarial networks—Part 1

4.7 Use generative adversarial networks—Part 2

4.8 Use a part of speech tagger

 

Lesson 5: Enhancing an Image Classifier

Learning objectives:

5.1 Start with torchvision.models

5.2 Retrain pretrained models

5.3 Modify network layer

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InformIT is the eCommerce home to Pearson technology-focused imprints including Addison-Wesley. We sell books, DRM-free eBooks, & video learning.

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. 

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  • Online Branch

    Online, Online, Online
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  • Teacher's Name
  • David Mertz
  • Teacher's Experience
  • David Mertz has been involved with the Python community for 20 years, with data science (under various earlier names), and with machine learning (since way back when it was more likely to be called “artificial intelligence”). He was a director of the Python Software Foundation for six years and continues to serve on, or chair, a variety of PSF working groups. He has also written quite a bit about Python: the column “Charming Python” for IBM developerWorks, for many years; the book Text Processing in Python (Addison-Wesley, 2003); and two short books for O’Reilly. He created the data science training program for Anaconda, Inc., and was a senior trainer for them.
  • Gender
  • Male
  • Teacher's Nationality
  • N/A
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