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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-in-practice-i/
课程评论：没有评论
Coursera 课程“深度学习实践 I：Tensorflow 基础和数据集” (Deep Learning in Practice I: Tensorflow Basics and Datasets) 旨在帮助你快速入门深度学习项目开发，无需深入钻研复杂的数学和理论，也无需应对繁琐的编程任务。 本课程是“深度学习实践”系列的第一部分，专注于教授如何使用深度神经网络进行分类项目，并指导你如何为工业级专业深度学习项目设计数据集。 课程强调实践，通过动手项目详细讲解从数据收集、加载、预处理到模型训练和评估的整个深度学习项目流程。讲师将分享组织和重用深度学习项目的最佳实践，让你能够实现一键生成结果，告别手动数据处理的低效。 学完本课程，你将能够高效地设计深度学习项目，并具备生成全面结果和可视化的能力，为后续更复杂的深度学习应用打下坚实基础。
You want to start developing deep learning solutions, but you do not want to lose time in mathematics and theory?You want to conduct deep learning projects, but do not like the hassle of tedious programming tasks?Do you want an automated process for developing deep learning solutions?This course is then designed for you! Welcome to Deep Learning in Practice, with NO PAIN!This course is the first course on a series of Deep Learning in Practice Courses of Anis Koubaa, namelyDeep Learning in Practice I: Tensorflow 2 Basics and Dataset Design (this course): the student will learn the basics of conducting a classification project using deep neural networks, then he learns about how to design a dataset for industrial-level professional deep learning projects. Deep Learning in Practice II: Transfer Learning and Models Evaluation: the student will learn how to manage complex deep learning projects and develop models using transfer learning using several state-of-the-art CNN algorithms. He will learn how to develop reusable projects and how to compare the results of different deep learning models in an automated manner. Deep Learning in Practice III: Face Recognition. The student will learn how to build a face recognition app in Tensorflow and Keras.Deep Learning in Practice I: Basics and Dataset DesignThere are plenty of courses and tutorials on deep learning. However, some practical skills are challenging to find in this massive bunch of deep learning resources, and that someone would spend a lot of time to get these practical skills.This course fills this gap and provides a series of practical lectures with hands-on projects through which I introduce the best practices that deep learning practitioners have to know to conduct deep learning projects.I have seen several people developing deep learning projects, but they fail to make their projects organized and reusable for other projects. This would lead to losing huge time when switching from one project to the others. In this course, I present several tips to efficiently structure deep learning projects that make you generate results in one simple click, instead of losing time into manual processing data collected from deep learning models.The hands-on projects explain in detail the whole loop of deep learning projects starting from data collection, to data loading, pre-processing, training, and evaluation.By the end of the course, you will be able to design deep learning projects in very little time with a comprehensive set of results and visualizations.
