In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.
Offert par
IBMÀ propos de ce cours
16 260 consultations récentes
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Certificat partageable
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Niveau avancé
Approx. 16 heures pour terminer
Anglais
Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Niveau avancé
Approx. 16 heures pour terminer
Anglais
Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol
Offert par
IBM
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.
Programme de cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
Module 1 - Loading Data
In this module, you will get introduced to the problem that we will try to solve throughout the course. You will also learn how to load the image dataset, manipulate images, and visualize them.
3 heures pour terminer
4 vidéos (Total 8 min)
3 heures pour terminer
Module 2
In this Module, you will mainly learn how to process image data and prepare it to build a classifier using pre-trained models.
3 heures pour terminer
1 vidéo (Total 2 min)
3 heures pour terminer
Module 3
In this Module, in the PyTorch part, you will learn how to build a linear classifier. In the Keras part, you will learn how to build an image classifier using the ResNet50 pre-trained model.
3 heures pour terminer
1 vidéo (Total 2 min)
7 heures pour terminer
Module 4
In this Module, in the PyTorch part, you will complete a peer review assessment where you will be asked to build an image classifier using the ResNet18 pre-trained model. In the Keras part, for the peer review assessment, you will be asked to build an image classifier using the VGG16 pre-trained model and compare its performance with the model that we built in the previous Module using the ResNet50 pre-trained model.
7 heures pour terminer
1 vidéo (Total 3 min), 2 lectures, 2 quiz
Avis
Meilleurs avis pour AI CAPSTONE PROJECT WITH DEEP LEARNING
par TG27 janv. 2021
Great course content! One thing that can be improved is the Skills Network portal. It's incredibly difficult to train any of the pre-trained models there.
par RK30 juil. 2020
The capstone of the project was really good it helped me to understand the deep learning concepts clearly for providing the solution.
par MP12 févr. 2021
The Keras part of the course is more attractive just because its final assignment is much better structured than that of PyTorch.
par DO26 mai 2020
I like the flexibility to pick our framework for the project i wish the kers one were a little bit more challenging
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