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.
ofrecido por
IBMAcerca de este Curso
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Fechas límite flexibles
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Certificado para compartir
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100 % en línea
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Nivel avanzado
Aprox. 16 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel avanzado
Aprox. 16 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
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.
Programa - Qué aprenderás en este curso
3 horas para completar
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 horas para completar
4 videos (Total 8 minutos)
3 horas para completar
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 horas para completar
1 video (Total 2 minutos)
3 horas para completar
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 horas para completar
1 video (Total 2 minutos)
7 horas para completar
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 horas para completar
1 video (Total 3 minutos), 2 lecturas, 2 cuestionarios
Reseñas
Principales reseñas sobre AI CAPSTONE PROJECT WITH DEEP LEARNING
por TG27 de ene. de 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.
por RK30 de jul. de 2020
The capstone of the project was really good it helped me to understand the deep learning concepts clearly for providing the solution.
por MP12 de feb. de 2021
The Keras part of the course is more attractive just because its final assignment is much better structured than that of PyTorch.
por DO26 de may. de 2020
I like the flexibility to pick our framework for the project i wish the kers one were a little bit more challenging
Preguntas Frecuentes
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