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Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning(으)로 돌아가기

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning, deeplearning.ai

4.6
(1,135개의 평가)

About this Course

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. The full deeplearning.ai TensorFlow Specialization will be available later this year, but you can get started with Course 1, Introduction to Tensorflow for AI, ML and DL, available now on Coursera....

최상위 리뷰

대학: AS

Mar 09, 2019

Good intro course, but google colab assignments need to be improved. And submitting a jupyter notebook was much more easier, why would I want to login to my google account to be a part of this course?

대학: DP

Apr 28, 2019

Excellent course material. easy walk through over complex coding. simple and fast teaching style of instructor. would love to do few more advanced courses by the same instructor.

필터링 기준:

239개의 리뷰

대학: Henry Wu

May 20, 2019

The course demystified simple computer vision classification use-cases by leveraging TensorFlow. This is a great follow-on course to Andrew Ng's 11-week Stanford Machine Learning course.

대학: Subhadeep Dash

May 19, 2019

Excellent Introduction to Tensorflow and Keras. Recommend it to everyone who wants to learn how to code various DL/ML techniques.

대학: Ivan Nedjalkov

May 19, 2019

I think this is a great way to introduce NN to people that have never seen one.

But there was very little depth in this course. I finished the 4 weeks in an afternoon. The external references were at times way too advanced, while the exercise code was way too simple. That being said, the Jupyter notebooks were a great material and helped me start with NN really quickly. The MNIST dataset is brilliant and hank you for showing how to do it.

The reason why I gave 3 stars is because the MOOCs aI have done in the past were much more extensive and gave plenty of theoretical background. Some people might think that the lack of theory lowers the entry bar for students, but in my book that's a tutorial not a course.

Save yourself the $40 price tag and buy a book on the topic, there are plenty out there.

대학: Jatinder Arora

May 19, 2019

Excellent course. I have been through multiple courses on ML and AI. This course is different due to its simplicity of explaining the concepts. Much Thanks to the instructor.

대학: Artem Batkivskyi

May 18, 2019

Great chance to put hands on Machine Learning with crystal clear lectures and convenient online practice in Google IDE.

대학: Trinadh

May 18, 2019

This is my specific review. I have done a lot of deep learning before and doing tensorflow , thought of getting rigorous exercises but there are only 2 examples. May be this is not the right course if you want to become expert in tensorflow, but it definitely has some organized information though to start off.

대학: Mahalingam.P.R

May 17, 2019

Beautiful

대학: Vinothkumar Ganeshan

May 16, 2019

Wonderful Course to begin with.

대학: Christian Leman

May 16, 2019

To the point, useful and reasonable time-commitment

대학: Daniel Müller

May 15, 2019

Nice course with some flaws. It’s a course in Keras with Tensorflow under the hood but you won’t see it. It’s great it’s Keras, however the title of the course is misleading. The videos are only a few minutes per week. Mostly it’s self-study on Google-Colaboratory. If you have no clue about Python or Machine Learning you might quickly be overwhelmed by the coding involved. If you have knowledge in Deep Learning you can earn this certificate within a few hours just by answering the (rather simple) quizzes even without watching the videos because the programming assignments are not graded. The course doesn’t have the depth of the Deep Learning Specialization by Andrew Ng but Keras is a great Deep Learning Library