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.
Walking through training the ConvNet with fit_generator
배우게 될 기술
Computer Vision, Tensorflow, Machine Learning
검토
RA
Jan 14, 2020
Learn alot from this course, specially the steps required from importing data to train data from simple to real world examples. Further explanation of each line of code also demonstrate thoroughly.
TW
Aug 13, 2019
There is quite some bugs in the example exercise code. Hope they can improve this in the future. Despite that, the content of the course is well designed and is a good start for beginners in TF
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!
강사:
Laurence Moroney
AI Advocate
Coursera 카탈로그 살펴보기
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