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This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data. The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models. Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on....

Top-Bewertungen

DL

28. Okt. 2020

a real eye opener education, it gave me lots of answers to the questions i had in this area. it is just amazing that ML can differ between roses and tulips !

MC

10. Dez. 2019

Great course, great team, First week is as it was expected but second is week is outstanding. The Neural Architecture search(NAS) is outstanding. Super job.

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