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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-deep-learning-artificial-neural-nets-with-python/
课程评论：没有评论
课程名称：使用Python进行实用深度学习与人工神经网络 课程概述： 本课程是一个专门定制的学习路径，将多个不同主题结合起来，帮助学员实现终极目标。深度学习是机器学习更高级实施的下一步，能够解决传统机器学习表现不佳的问题，如从图像中检测和提取对象、从文本中提取意义以及基于复杂依赖关系预测结果等。在这个实用的学习路径中，学员将使用真实世界的数据集和Python构建深度学习应用程序。 课程将采取循序渐进的方法，从构建神经网络开始，再到强化学习，以及处理计算机视觉、语音和图像识别等不同深度学习应用。本课程旨在指导学员入门深度学习概念，并提供一系列简单实用的解决方案，帮助学员学习如何将所学技能应用于日常问题。 课程结束时，学员将能够应用深度学习概念，利用Python解决基于真实世界数据集的挑战性任务。 课程特点： - 从头开始学习深度学习，分层构建复杂模型，迅速掌握。 - 涉及Python下的深度学习常见及不常见问题的实践指南。 - 通过实际的真实案例，快速探索深度学习的实用本质。 授课讲师： Radhika Datar拥有超过6年的软件开发和内容写作经验，对Python、PHP和Java等框架非常熟悉。自2016年6月起，她在Educba和Eduonix担任培训顾问，同时自2015年9月起担任TutorialsPoint的学术撰稿人。 Jakub Konczyk自1995年起便开始从事编程工作，精通Python和Django，自2006年起参与复杂系统的构建。他热爱简化和教授编程主题，并愿意与他人分享。机器学习的初次接触源于他在一家初创公司尝试预测房地产价格的经历。尽管失败，但他后来发现了更实用的学习机器学习的方法，并分享在本课程中。
Video Learning Path OverviewA Learning Path is a specially tailored course that brings together two or more different topics that lead you to achieve an end goal. Much thought goes into the selection of the assets for a Learning Path, and this is done through a complete understanding of the requirements to achieve a goal.Deep learning is the next step to a more advanced implementation of Machine Learning. Deep Learning allows you to solve problems where traditional Machine Learning methods might perform poorly: detecting and extracting objects from images, extracting meaning from text, and predicting outcomes based on complex dependencies, to name a few.In this practical Learning Path, you will build Deep Learning applications with real-world datasets and Python. Beginning with a step by step approach, right from building your neural nets to reinforcement learning and working with different Deep Learning applications such as computer Vision and voice and image recognition, this course will be your guide in getting started with Deep Learning concepts.Moving further with simple and practical solutions provided, we will cover a whole range of practical, real-world projects that will help customers learn how to implement their skills to solve everyday problems.By the end of the course, you'll apply Deep Learning concepts and use Python to solve challenging tasks with real-world datasets.Key FeaturesGet started with Deep Learning and build complex models layer by layer, with increasing complexity, in no time.A hands-on guide covering common as well as not-so-common problems in deep learning using Python.Explore the practical essence of Deep Learning in a relatively short amount of time by working on practical, real-world use cases.Author BiosRadhika Datar has more than 6 years' experience in Software Development and Content Writing. She is well versed with frameworks such as Python, PHP, and Java and regularly provides training on them. She has been working with Educba and Eduonix as a Training Consultant since June 2016 and has been an Academic writer with TutorialsPoint since Sept 2015.Jakub Konczyk has enjoyed and done programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share it with others. He first discovered Machine Learning when he was trying to predict the real estate prices in one of the early stage start-ups he was involved in. He failed miserably but then discovered a much more practical way to learn Machine Learning that he shares in this course.
