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An Introduction to Practical Deep Learning
  • Level Professional
  • Duration 17 hours
  • Course by Intel
  • Offered by

About

This course provides an introduction to Deep Learning, a field that aims to harness the enormous amounts of data that we are surrounded by with artificial neural networks, allowing for the development of self-driving cars, speech interfaces, genomic sequence analysis and algorithmic trading. You will explore important concepts in Deep Learning, train deep networks using Intel Nervana Neon, apply Deep Learning to various applications and explore new and emerging Deep Learning topics.

Modules

Introduction to Deep Learning

    • 2 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Exercise 1

2 Videos

  • Introduction to Deep Learning
  • Exercise 1: Introduction to Deep Learning

1 Readings

  • Welcome!

Introduction to Deep Learning: Additional Resources (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Additional Resources (Optional)

Deep Learning Basics

    • 2 Videos
    • 3 Lab
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3 Labs

  • Exercise 2a
  • Exercise 2b
  • Exercise 2c

2 Videos

  • Deep Learning Basics
  • Exercise 2: Deep Learning Basics

Deep Learning Basics: Additional Resources (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Additional Resources (Optional)

Quiz: Introduction to Deep Learning and Deep Learning Basics

    • 1 Assignment
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1 Assignment

  • Introduction to Deep Learning and Deep Learning Basics

Convolutional Neural Networks

    • 2 Videos
    • 2 Lab
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2 Labs

  • Exercise 3a
  • Exercise 3b

2 Videos

  • Convolutional Neural Networks
  • Exercise 3: Convolutional Neural Networks

Convolutional Neural Networks: Additional Resources (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Additional Resources (Optional)

Fine-Tuning and Detection

    • 2 Videos
    • 1 Lab
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1 Labs

  • Exercise 4a

2 Videos

  • Fine-Tuning and Detection
  • Exercise 4: Fine-Tuning and Detection

Fine-Tuning and Detection: Additional Resources (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Additional Resources (Optional)

Quiz: Convolutional Neural Networks, Fine-Tuning and Detection

    • 1 Assignment
Show info about module content

1 Assignment

  • Convolutional Neural Networks, Fine-Tuning and Detection

Recurrent Neural Networks

    • 2 Videos
    • 2 Lab
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2 Labs

  • Exercise 5a
  • Exercise 5b

2 Videos

  • Recurrent Neural Networks
  • Exercise 5: Recurrent Neural Networks

Recurrent Neural Networks: Additional Resources (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Additional Resources (Optional)

Quiz: Recurrent Neural Networks

    • 1 Assignment
Show info about module content

1 Assignment

  • Recurrent Neural Networks

Training Tips

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Exercise 6

2 Videos

  • Training Tips
  • Exercise 6: Training Tips

Training Tips: Additional Resources (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Additional Resources (Optional)

Multinode Distributed Training

    • 1 Videos
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1 Videos

  • Multinode Distributed Training

Multinode Distributed Training: Additional Resources (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Additional Resources (Optional)

Quiz: Training Tips and Multinode Distributed Training

    • 1 Assignment
Show info about module content

1 Assignment

  • Training Tips and Multinode Distributed Training

Hot Research

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Exercise 8

2 Videos

  • Hot Research
  • Exercise 8: Reinforcement Learning

Hot Research: Additional Resources (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Additional Resources (Optional)

Intel's Roadmap

    • 1 Videos
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1 Videos

  • Intel's Roadmap

Intel's Roadmap: Additional Resources (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Additional Resources (Optional)

Final Quiz

    • 1 Assignment
Show info about module content

1 Assignment

  • Final Quiz

Auto Summary

"An Introduction to Practical Deep Learning" is an IT & Computer Science course on Coursera, led by expert instructors. This professional-level course delves into harnessing data with artificial neural networks for applications like self-driving cars and algorithmic trading. Over 1020 minutes, students will train deep networks using Intel Nervana Neon and explore emerging Deep Learning topics. Subscription options include Starter, Professional, and Paid plans, catering to learners eager to delve into the cutting-edge field of Deep Learning.

Andres Rodriguez
Instructors

Andres Rodriguez

Nikhil Murthy
Instructors

Nikhil Murthy

Hanlin Tang
Instructors

Hanlin Tang