If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
Defining a ConvNet to use complex images
Получаемые навыки
Computer Vision, Tensorflow, Machine Learning
Рецензии
I give this course 5 stars because of what I'm being able to learn within just a little amount of time. I would highly recommend this course to anyone who wishes to participate, it worth the effort!
I would say that I have dabbled in Python and ML before taking this course. I even finished the IBM Data Science Professional Certification. I was very much able to grasp what was being taught here.
Last week you saw how to improve the results from your deep neural network using convolutions. It was a good start, but the data you used was very basic. What happens when your images are larger, or if the features aren’t always in the same place? Andrew and Laurence discuss this to prepare you for what you’ll learn this week: handling complex images!
Преподаватели
Laurence Moroney
Instructor
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