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.
Этот курс входит в специализацию ''Профессиональная сертификация 'TensorFlow-разработчик от deeplearning.ai''
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Об этом курсе
Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.
Чему вы научитесь
Learn best practices for using TensorFlow, a popular open-source machine learning framework
Build a basic neural network in TensorFlow
Train a neural network for a computer vision application
Understand how to use convolutions to improve your neural network
Приобретаемые навыки
- Computer Vision
- Tensorflow
- Machine Learning
Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.
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deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Программа курса: что вы изучите
A New Programming Paradigm
Welcome to this course on going from Basics to Mastery of TensorFlow. We're excited you're here! In Week 1, you'll get a soft introduction to what Machine Learning and Deep Learning are, and how they offer you a new programming paradigm, giving you a new set of tools to open previously unexplored scenarios. All you need to know is some very basic programming skills, and you'll pick the rest up as you go along. To get started, check out the first video, a conversation between Andrew and Laurence that sets the theme for what you'll study...
Introduction to Computer Vision
Welcome to week 2 of the course! In week 1 you learned all about how Machine Learning and Deep Learning is a new programming paradigm. This week you’re going to take that to the next level by beginning to solve problems of computer vision with just a few lines of code!
Enhancing Vision with Convolutional Neural Networks
Welcome to week 3! In week 2 you saw a basic Neural Network for Computer Vision. It did the job nicely, but it was a little naive in its approach. This week we’ll see how to make it better, as discussed by Laurence and Andrew here.
Using Real-world Images
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!
Рецензии
- 5 stars80,37 %
- 4 stars15,84 %
- 3 stars2,51 %
- 2 stars0,67 %
- 1 star0,59 %
Лучшие отзывы о курсе INTRODUCTION TO TENSORFLOW FOR ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING
Very well organized. Good speakers. Content is comprehensive for a Introductory Course. A little more explanation on Validation versus Testing and on some of the evaluation functions would be helpful.
This course helps to dive me into the world of Neural Network . I have learned a lot though my Course . It was very interesting and fun learning with Coursera . Looking forward for the next Course.
It's a good hands-on exercise. I like to see more link to keras api document when we introduce new function in keras. However, Tensorflow document regarding keras api is yet in complete. Thank you.
This is an introductory course on TensorFlow. The pace of the course is really good. I appreciate the pain that Prof. Andrew N G and Prof. Laurence have taken to construct and deliver this course.
Профессиональная сертификация 'TensorFlow-разработчик от deeplearning.ai': общие сведения
TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models.

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