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所在平台: Udemy |
课程主页: https://www.udemy.com/course/everything-about-convolutional-neural-networks-2022/
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课程名称：关于卷积神经网络的一切 课程概述：您是否在寻找一门完整的卷积神经网络（CNN）课程，学习如何在Python中创建图像分类模型？您找对地方了！完成本课程后，您将能够识别可以通过CNN模型解决的图像分类问题，使用Keras和Tensorflow库在Python中创建CNN模型并分析其结果，自信地实践、讨论和理解深度学习概念，清晰了解神经网络的内部运作及相关概念。 课程价值：所有参加此卷积神经网络课程的学生均获得可验证的完成证书。如果您是数据分析师、机器学习科学家，或希望在现实世界的图像识别问题中学习和应用深度学习的学生，本课程将为您提供坚实的基础，教授一些先进的深度学习概念及其在Python中的实施，而无需过多的数学内容。 选择本课程的理由：本课程涵盖创建图像分类模型的所有步骤，而大多数课程仅关注如何运行分析。我们相信，扎实的理论理解能够帮助我们创建优秀的模型。在分析运行之后，能够评估模型的质量并解释结果对于实际帮助业务至关重要。 课程内容包括： - 理解深度学习 - 激活函数 - 神经网络的工作原理与学习方式 - 梯度下降与随机梯度下降 - CNN的构建与评估 - 实践项目 每节课都有课堂笔记可供跟随，同时有一个最终的实践作业，以帮助您实际应用所学知识。点击注册按钮，让我们在第一课见！
You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create an Image Classification model in Python, right?You've found the right Convolutional Neural Networks course!After completing this course you will be able to:Identify the Image Classification problems which can be solved using CNN Models.Create CNN models in Python using Keras and Tensorflow libraries and analyze their results.Confidently practice, discuss and understand Deep Learning conceptsHave a clear understanding of how Neural Networks work internally, and what are various concepts related to this niche.How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.Why should you choose this course?This course covers all the steps that one should take to create an image classification model using Convolutional Neural Networks.Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model. And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.Download Practice filesWith each lecture, there are class notes attached for you to follow along. There is a final practical assignment for you to practically implement your learning.What is covered in this course?Understanding Deep LearningActivation FunctionsHow Neural Network works & learnsGradient Descent vs Stochastic Gradient DescentCNN - Building & Evaluating a modelHands-on ProjectGo ahead and click the enroll button, and I'll see you in lesson 1!
