The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems.
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IBM Skills NetworkAbout this Course
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Intermediate Level
Approx. 7 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessFlexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level
Approx. 7 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Could your company benefit from training employees on in-demand skills?
Try Coursera for Business
Offered by
IBM Skills Network
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Syllabus - What you will learn from this course
2 hours to complete
Introduction
In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models.
2 hours to complete
5 videos (Total 28 min), 2 readings, 5 quizzes
1 hour to complete
Supervised Learning Models
In this module, you will learn about about Convolutional Neural Networks, and the building blocks of a convolutional neural network, such as convolution and feature learning. You will also learn about the popular MNIST database. Finally, you will learn how to build a Multi-layer perceptron and convolutional neural networks in Python and using TensorFlow.
1 hour to complete
3 videos (Total 22 min)
2 hours to complete
Supervised Learning Models (Cont'd)
In this module, you will learn about the recurrent neural network model, and special type of a recurrent neural network, which is the Long Short-Term Memory model. Also, you will learn about the Recursive Neural Tensor Network theory, and finally, you will apply recurrent neural networks to language modelling.
2 hours to complete
4 videos (Total 22 min)
1 hour to complete
Unsupervised Deep Learning Models
In this module, you will learn about the applications of unsupervised learning. You will learn about Restricted Boltzmann Machines (RBMs), and how to train an RBM. Finally, you will apply Restricted Boltzmann Machines to build a recommendation system.
1 hour to complete
2 videos (Total 10 min)
Reviews
- 5 stars61.18%
- 4 stars23.85%
- 3 stars9.33%
- 2 stars2.81%
- 1 star2.81%
TOP REVIEWS FROM BUILDING DEEP LEARNING MODELS WITH TENSORFLOW
by DOMay 26, 2020
Not so often i wish a course would be longer and more in depth I really enjoyed using TF I'll look some other courses about it
by LDNov 5, 2019
course needed to be updated for labs. Now Google moved to Tensorflow 2.0 this year.
by PCMar 31, 2020
The coding part was hard to understand. If that part could also be covered in videos as a tutorial.
by MKNov 21, 2019
Good content. A bit too fast on some complex concepts and missing audio for the last lecture but great lecturer.
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