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.
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IBM 技能网络課程信息
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高級
完成時間大約為16 小時
英語（English）
字幕：法語（French）, （歐洲人講的）葡萄牙語, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
高級
完成時間大約為16 小時
英語（English）
字幕：法語（French）, （歐洲人講的）葡萄牙語, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
提供方
IBM 技能网络
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.
授課大綱 - 您將從這門課程中學到什麼
完成時間為 3 小時
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.
完成時間為 3 小時
4 個視頻 （總計 8 分鐘）
完成時間為 3 小時
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.
完成時間為 3 小時
1 個視頻 （總計 2 分鐘）
完成時間為 3 小時
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.
完成時間為 3 小時
1 個視頻 （總計 2 分鐘）
完成時間為 7 小時
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.
完成時間為 7 小時
1 個視頻 （總計 3 分鐘）, 2 個閱讀材料, 2 個測驗
審閱
- 5 stars69.53%
- 4 stars19.41%
- 3 stars7.61%
- 2 stars1.71%
- 1 star1.71%
來自AI CAPSTONE PROJECT WITH DEEP LEARNING 的熱門評論
由 TS 提供2020年3月26日
Thank a lot for creating this course. It really useful and practical for me.
由 DO 提供2020年5月26日
I like the flexibility to pick our framework for the project i wish the kers one were a little bit more challenging
由 LD 提供2020年2月19日
But need to study extra as these topics are not taught like Transfer Learning
由 MP 提供2021年2月12日
The Keras part of the course is more attractive just because its final assignment is much better structured than that of PyTorch.
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