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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ai
课程评论：没有评论
课程名称：应用人工智能与深度学习 概述： 报名参加本课程即表示您同意FAQ中列出的最终用户许可协议。注册后，您可以在资源区域访问该许可。本课程是IBM高级数据科学证书的一部分，提供了对深度学习模型的深入见解，这些模型在自然语言处理、计算机视觉、时间序列分析以及其他多个学科中广泛应用。我们将学习线性代数和神经网络的基础知识。接下来，介绍诸如Keras、TensorFlow、PyTorch、DeepLearning4J和Apache SystemML等最流行的深度学习框架，Keras和TensorFlow将在本课程中占据最大部分。 我们将通过基于Keras构建模型来学习异常检测、时间序列预测、图像识别和自然语言处理，案例来源于物联网(IoT)、金融市场数据、文学作品和图像数据库。最后，我们将学习如何使用Kubernetes、Apache Spark和GPU来扩展这些人工智能模型。 重要信息：单独修读此课程无法获得“IBM Watson IoT认证数据科学家证书”。您需要参加另外三门课程，其中两门课程目前正在建设中，该专业化课程预计在2018年春末或夏初完工。 无论您在想要掌握的主题上处于何种技能水平，利用这些方法都可以改变您的思维和生活。如果您已经是专家，这将为您提供加速成功创建和部署深度学习模型的思路。如果您正在挣扎，您将看到一整套结构化的实用技术，帮助您走上正轨。如果您渴望在任何领域变得更好，本课程将为您提供指导。 先决条件：需要具备一定的编程技能，最好是Python，但其他编程语言也可以。同时对数学（线性代数）的基本了解将是一个加分项，但我们将在第一周涵盖这一部分内容。 如果您选择参加此课程并获得Coursera课程证书，您还将获得IBM数字徽章。欲了解更多关于IBM数字徽章的信息，请访问链接 ibm.biz/badging。 课程大纲： 1. 第一部分：深度学习简介 2. 第二部分：深度学习框架 3. 第三部分：深度学习应用 4. 第四部分：扩展与部署
Part: 1
Title:Introduction to deep learning
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Part: 2
Title:DeepLearning Frameworks
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Part: 3
Title:DeepLearning Applications
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Part: 4
Title:Scaling and Deployment
Description:
>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<< This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs. IMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE "IBM Watson IoT Certified Data Scientist certificate". You need to take three other courses where two of them are currently built. The Specialization will be ready late spring, early summer 2018 Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of DeepLearning models. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide. Prerequisites: Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.
