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Convolutional Neural Networks in TensorFlow に戻る による Convolutional Neural Networks in TensorFlow の受講者のレビューおよびフィードバック



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. In Course 2 of the TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models. 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 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....




A really good course that builds up the knowledge over the concepts covered in Course 1. All the ideas are applicable in real world scenario and this is what makes the course that much more valuable!



Nice experience taking this course. Precise and to the point introduction of topics and a really nice head start into practical aspects of Computer Vision and using the amazing tensorflow framework..


Convolutional Neural Networks in TensorFlow: 1151 - 1158 / 1,158 レビュー

by Jakob A


N​otebooks don't match lessons. where the fuck did week 4 assignment come from? It has nothing to do with multi-class classification and uses several techinques never covered.

by Samiul B


Very less lecture videos, most are reading based. By reading, it is hard to understand the topic and code implementatio.

by Matthew F


Poorly constructed labs in this one, made it take much longer than needed to understand the course content.

by Anders S P


Spent 10 times as much time making the flakey grader code happy than actually learning the subject

by Gabriel S


no graded exercice

the rest is good but without graded exercice it's hard to really put it to work



poorly designed assignments and not much learned

by Aniket D B


Vague Assignment instructions.

by Alex D


Extremely basic