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.
Acerca de este Curso
Habilidades que obtendrás
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
- 5 stars68,42 %
- 4 stars23 %
- 3 stars6,43 %
- 2 stars1,75 %
- 1 star0,38 %
Principales reseñas sobre IMAGE UNDERSTANDING WITH TENSORFLOW ON GCP
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.
Good Practical Experience with the concepts what that I learned . Good for recommending my friends.
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 !
You should improve the explanation of Transfer Learning from prebuilt models like ResNet. The conceptual side is not clear.
Acerca de Programa especializado: Advanced Machine Learning on Google Cloud
This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems. Topics introduced in earlier courses are referenced in later courses, so it is recommended that you take the courses in exactly this order.
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