Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

开始时间: 04/22/2022 持续时间: Unknown

所在平台: CourseraArchive

课程类别: 计算机科学

大学或机构: CourseraNew

课程主页: https://www.coursera.org/archive/introduction-tensorflow

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课程详情

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.

用于人工智能,机器学习和深度学习的TensorFlow简介:如果您是想要构建可扩展的AI驱动算法的软件开发人员,则需要了解如何使用这些工具来构建它们。本课程是Tensorflow专业化中即将进行的机器学习的一部分,将教您使用TensorFlow(一种流行的机器学习开源框架)的最佳实践。 Ng的机器学习课程和深度学习专业知识教授了机器学习和深度学习的最重要和最基本的原理。这个新的deeplearning.ai TensorFlow专业化课程教您如何使用TensorFlow实施这些原理,以便您可以开始构建可伸缩模型并将其应用于实际问题。为了更深入地了解神经网络的工作原理,建议您参加“深度学习专业化”课程。

课程大纲

A New Programming Paradigm
Introduction to Computer Vision
Enhancing Vision with Convolutional Neural Networks
Using Real-world Images

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课程简介

面向人工智能、机器学习和深度学习的TensorFlow导引 (Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning) ,该课程由Andrew Ng的deepIearning.ai和Google TensorFlow团队联合出品,涵盖一种新的编程范式、计算机视觉简介、利用卷积神经网络增强视觉学习、使用真实世界的图像等章节,感兴趣的同学可以关注: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 Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

课程标签

Google 机器学习 深度学习 Deep-Learning 人工智能 Tensorflow

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