About this Course
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Flexible deadlines

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Intermediate Level

Course 1 of the TensorFlow Specialization, Python coding, and high-school level math are required. ML/DL experience is helpful but not required.

Approx. 7 hours to complete

Suggested: 4 weeks of study, 4-5 hours/week...

English

Subtitles: English

What you will learn

  • Check

    Handle real-world image data

  • Check

    Plot loss and accuracy

  • Check

    Explore strategies to prevent overfitting, including augmentation and dropout

  • Check

    Learn transfer learning and how learned features can be extracted from models

Skills you will gain

Inductive TransferAugmentationDropoutsMachine LearningTensorflow

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Course 1 of the TensorFlow Specialization, Python coding, and high-school level math are required. ML/DL experience is helpful but not required.

Approx. 7 hours to complete

Suggested: 4 weeks of study, 4-5 hours/week...

English

Subtitles: English

Learners taking this Course are

  • Machine Learning Engineers
  • Data Scientists
  • Chief Technology Officers (CTOs)
  • Biostatisticians
  • Data Engineers

Syllabus - What you will learn from this course

Week
1
4 hours to complete

Exploring a Larger Dataset

8 videos (Total 18 min), 5 readings, 3 quizzes
8 videos
Working through the notebook4m
Fixing through cropping49s
Visualizing the effect of the convolutions1m
Looking at accuracy and loss1m
Week 1 Wrap up33s
5 readings
Before you Begin: TensorFlow 2.0 and this Course10m
The cats vs dogs dataset10m
Looking at the notebook10m
What you'll see next10m
What have we seen so far?10m
1 practice exercise
Week 1 Quiz30m
Week
2
4 hours to complete

Augmentation: A technique to avoid overfitting

7 videos (Total 14 min), 6 readings, 3 quizzes
7 videos
Demonstrating overfitting in cats vs. dogs1m
Adding augmentation to cats vs. dogs1m
Exploring augmentation with horses vs. humans1m
Week 2 Wrap up37s
6 readings
Image Augmentation10m
Start Coding...10m
Looking at the notebook10m
The impact of augmentation on Cats vs. Dogs10m
Try it for yourself!10m
What have we seen so far?10m
1 practice exercise
Week 2 Quiz30m
Week
3
4 hours to complete

Transfer Learning

7 videos (Total 14 min), 5 readings, 3 quizzes
7 videos
Coding your own model with transferred features2m
Exploring dropouts1m
Exploring Transfer Learning with Inception1m
Week 3 Wrap up36s
5 readings
Start coding!10m
Adding your DNN10m
Using dropouts!10m
Applying Transfer Learning to Cats v Dogs10m
What have we seen so far?10m
1 practice exercise
Week 3 Quiz30m
Week
4
4 hours to complete

Multiclass Classifications

6 videos (Total 12 min), 5 readings, 3 quizzes
6 videos
Train a classifier with Rock Paper Scissors1m
Test the Rock Paper Scissors classifier2m
A conversation with Andrew Ng1m
5 readings
Introducing the Rock-Paper-Scissors dataset10m
Check out the code!10m
Try testing the classifier10m
What have we seen so far?10m
Wrap up10m
1 practice exercise
Week 4 Quiz30m
4.7
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Top reviews from Convolutional Neural Networks in TensorFlow

By MHMay 24th 2019

A very comprehensive and easy to learn course on Tensor Flow. I am really impressed by the Instructor ability to teach difficult concept with ease. I will look forward another course of this series.

By CMMay 1st 2019

A patient and coherent introduction. At the end, you have good working code you can use elsewhere. Remarkably, the primary lecturer, Laurence Moroney, responds fairly quickly to posts in the forum.

Instructor

Avatar

Laurence Moroney

AI Advocate
Google Brain

About deeplearning.ai

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

About the TensorFlow in Practice Specialization

Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework. In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as you train it to identify real-world images. You’ll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry! AI is already transforming industries across the world. After finishing this Specialization, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. Courses 1-3 are available now, with Course 4 launching in July....
TensorFlow in Practice

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.