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所在平台: Udemy |
课程主页: https://www.udemy.com/course/applied-deep-learning-with-keras/
课程评论：没有评论
课程名称：应用深度学习与Keras 课程概述：设计神经网络是一项备受追求的技能，但掌握起来并不简单。通过Keras，您可以用最少的代码应用复杂的机器学习算法。《应用深度学习与Keras》课程将带您从机器学习和Python的基础知识开始，一直到深入理解如何应用Keras开发高效的深度学习解决方案。课程将指导您建立逻辑回归模型，首先使用scikit-learn，然后使用Keras，以帮助您理解机器学习与深度学习的区别。 您将深入探讨Keras及其多种模型，通过创建预测模型来应对不同的现实场景，包括疾病预测和客户流失。您将学习如何评估、优化和改进模型，以实现最大的信息获取。接下来，您将通过使用Keras Wrapper和scikit-learn进行交叉验证来评价模型。 在此之后，您将了解如何应用L1、L2和dropout正则化技术来提高模型的准确性。为了维护准确性，您将掌握包括零准确性、精确度和AUC-ROC分数等技术，以进行模型的微调。课程结束时，您将具备在构建高级深度神经网络时使用Keras所需的技能。 关于讲师： - Ritesh Bhagwat拥有应用数学硕士学位，专业于计算机科学，拥有超过14年的数据驱动技术经验，目前担任数据科学家。 - Mahla Abdolahnejad是加拿大卡尔顿大学系统与计算机工程的博士候选人，专注于深度无监督学习在计算机视觉应用中的研究。 - Matthew Moocarme是Viacom广告科学团队的主任和高级数据科学家，专注于利用数据科学和机器学习设计数据驱动解决方案，拥有物理学博士学位。 通过本课程，您将能够掌握深度学习与Keras的应用，将理论知识转化为实践技能。
Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code.Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the course guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You'll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you'll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you'll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you'll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.By the end of this course, you will have the skills you need to use Keras when building high-level deep neural networks.About the AuthorRitesh Bhagwat has a master's degree in applied mathematics with a specialization in computer science. He has over 14 years of experience in data-driven technologies and has led and been a part of complex projects ranging from data warehousing and business intelligence to machine learning and artificial intelligence. He has worked with top-tier global consulting firms as well as large multinational financial institutions. Currently, he works as a data scientist. Besides work, he enjoys playing and watching cricket and loves to travel. He is also deeply interested in Bayesian statistics.Mahla Abdolahnejad is a Ph.D. candidate in systems and computer engineering with Carleton University, Canada. She also holds a bachelor's degree and a master's degree in biomedical engineering, which first exposed her to the field of artificial intelligence and artificial neural networks, in particular. Her Ph.D. research is focused on deep unsupervised learning for computer vision applications. She is particularly interested in exploring the differences between a human's way of learning from the visual world and a machine's way of learning from the visual world, and how to push machine learning algorithms toward learning and thinking like humans.Matthew Moocarme is a director and senior data scientist in Viacom's Advertising Science team. As a data scientist at Viacom, he designs data-driven solutions to help Viacom gain insights, streamline workflows, and solve complex problems using data science and machine learning.Matthew lives in New York City and outside of work enjoys combining deep learning with music theory. He is a classically-trained physicist, holding a Ph.D. in Physics from The Graduate Center of CUNY and is an active Artificial Intelligence developer, researcher, practitioner, and educator.
