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Convolutional Neural Networks in TensorFlow
  • Level Professional
  • المدة 17 ساعات hours
  • الطبع بواسطة DeepLearning.AI
  • Offered by

عن

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer "sees" information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

الوحدات

Introduction

    • 1 Videos
    • 1 Readings
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1 Videos

  • Introduction: A conversation with Andrew Ng

1 Readings

  • Welcome to the course!

Larger Dataset

    • 7 Videos
    • 4 Readings
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Week 1 Quiz

1 Labs

  • Looking at the notebook (Lab 1)

7 Videos

  • A conversation with Andrew Ng
  • Training with the cats vs. dogs dataset
  • Working through the notebook
  • Fixing through cropping
  • Visualizing the effect of the convolutions
  • Looking at accuracy and loss
  • Week 1 Wrap up

4 Readings

  • The cats vs dogs dataset
  • About the notebooks in this course
  • [IMPORTANT] Have questions, issues or ideas? Join our Forum!
  • What have we seen so far?

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes Week 1

Weekly Assignment - Attempt the cats vs. dogs Kaggle challenge!

    • 2 Readings
    • 1 Programming
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1 Programming

  • Cats vs Dogs

2 Readings

  • Assignment Troubleshooting Tips
  • (Optional) Downloading your Notebook and Refreshing your Workspace

Augmentation

    • 7 Videos
    • 3 Readings
    • 2 Lab
    • 1 Assignment
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1 Assignment

  • Week 2 Quiz

2 Labs

  • Looking at the notebook (Lab 1)
  • Image Augmentation with Horses vs Humans! (Lab 2)

7 Videos

  • A conversation with Andrew Ng
  • Introducing augmentation
  • Coding augmentation with the Layers API
  • Demonstrating overfitting in cats vs. dogs
  • Adding augmentation to cats vs. dogs
  • Exploring augmentation with horses vs. humans
  • Week 2 Wrap up

3 Readings

  • Image Augmentation
  • Start Coding...
  • What have you seen so far?

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes Week 2

Weekly Assignment - Full cats vs. dogs using augmentation

    • 1 Programming
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1 Programming

  • Cats vs Dogs with Data Augmentation

Transfer Learning

    • 7 Videos
    • 3 Readings
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Week 3 Quiz

1 Labs

  • Applying Transfer Learning to Cats v Dogs (Lab 1)

7 Videos

  • A conversation with Andrew Ng
  • Understanding transfer learning: the concepts
  • Coding transfer learning from the inception model
  • Coding your own model with transferred features
  • Exploring dropouts
  • Exploring Transfer Learning with Inception
  • Week 3 Wrap up

3 Readings

  • Adding your DNN
  • Using dropout!
  • What have you seen so far?

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes Week 3

Weekly Assignment - Transfer Learning: Horses vs Humans

    • 1 Programming
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1 Programming

  • Transfer Learning - Horses or Humans

Multiclass Classifications

    • 5 Videos
    • 3 Readings
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Week 4 Quiz

1 Labs

  • Check out the code! (Lab 1)

5 Videos

  • A conversation with Andrew Ng
  • Moving from binary to multi-class classification
  • Explore multi-class with Rock Paper Scissors dataset
  • Train a classifier with Rock Paper Scissors
  • Test the Rock Paper Scissors classifier

3 Readings

  • Introducing the Rock-Paper-Scissors dataset
  • Try testing the classifier
  • What have you seen so far?

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes Week 4

End of Access to Lab Notebooks

    • 1 Readings
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1 Readings

  • [IMPORTANT] Reminder about end of access to Lab Notebooks

Weekly Assignment - Multi-class Classification

    • 1 Programming
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1 Programming

  • Classification: Beyond two classes

Course 2 Wrap up

    • 1 Videos
    • 1 Readings
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1 Videos

  • A conversation with Andrew Ng

1 Readings

  • Wrap up

Acknowledgments

    • 1 Readings
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1 Readings

  • Acknowledgments

Auto Summary

Dive into "Convolutional Neural Networks in TensorFlow," a professional-level course designed for software developers aiming to build scalable AI algorithms. Led by DeepLearning.AI and part of the TensorFlow Developer Specialization, this course focuses on advanced computer vision techniques. Learn to handle real-world images, visualize convolutions, and implement strategies like augmentation and dropout. With a duration of approximately 17 hours, flexible subscription options are available. Ideal for those familiar with foundational machine learning principles, this course helps you apply TensorFlow to solve real-world problems.

Laurence Moroney
Instructor

Laurence Moroney