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所在平台: Udemy |
课程主页: https://www.udemy.com/course/hands-on-keras-for-machine-learning-engineers/
课程评论：没有评论
课程名称：Keras实践：机器学习工程师的实用指南 课程概述： 欢迎来到“Keras实践：机器学习工程师的实用指南”。本课程是学习Python中使用Keras进行深度学习的全面指南。您将探索Keras这一深度学习的Python库，学习如何开发和评估深度学习模型。 Keras是一个简单易用的库，它封装了Theano和TensorFlow的复杂性，提供了一个简洁的API。Theano由蒙特利尔大学开发，TensorFlow则由谷歌开发，这两个平台都是用于Python中的深度学习模型开发。Keras已经成为快速原型开发深度学习模型的黄金标准。 课程简介： 本课程适合开发者、机器学习工程师和数据科学家，特别是那些希望充分利用Keras的人。虽然您不需要成为机器学习专家，但了解一些基本的机器学习概念（如交叉验证和独热编码）会很有帮助。本课程是Keras库的入门级教程。 学习内容： - 如何端到端地开发和评估神经网络模型 - 更高级的技术，用于开发最前沿的深度学习模型 - 构建用于图像和文本数据的大型模型 - 使用先进的图像增强技术提高模型性能 - 深度学习在Python中的帮助资源 - Keras模型的基础结构 - 评估Keras深度学习模型的性能 - 构建端到端的回归和分类模型 - 通过检查点保存最佳模型 - 通过Dropout正则化减少过拟合 - 使用学习率调度提高性能 - 快速入门卷积神经网络 本课程是一个实践指南，旨在通过动手实践让您学习，您将能够将新获得的知识应用于自己的深度学习Keras模型中。为了充分利用本课程，建议您在每个教程中逐步完成所有的示例，而不是像观看电影一样观看课程。 感谢您对“Keras实践：机器学习工程师的实用指南”的关注。让我们开始吧！
** Mike's courses are popular with many of our clients." Josh Gordon, Developer Advocate, Google **"This is well developed with an appropriate level of animation and illustration." - Bruce"Very good course for somebody who already has pretty good foundation in machine learning." - Il-Hyung ChoWelcome to Hands-On Keras for Machine Learning Engineers. This course is your guide to deep learning in Python with Keras. You will discover the Keras Python library for deep learning and how to use it to develop and evaluate deep learning models.There are two top numerical platforms for developing deep learning models, they are Theano developed by the University of Montreal and TensorFlow developed at Google. Both were developed for use in Python and both can be leveraged by the super simple to use Keras library. Keras wraps the numerical computing complexity of Theano and TensorFlow providing a concise API that we will use to develop our own neural network and deep learning models. Keras has become the gold standard in the applied space for rapid prototyping deep learning models. My name is Mike West and I'm a machine learning engineer in the applied space. I've worked or consulted with over 50 companies and just finished a project with Microsoft. I've published over 50 courses and this is 55 on Udemy. If you're interested in learning what the real-world is really like then you're in good hands.Who is this course for? This course is for developers, machine learning engineers and data scientists that want to learn how to get the most out of Keras. You do not need to be a machine learning expert, but it would be helpful if you knew how to navigate a small machine learning problem using SciKit-Learn. Basic concepts like cross-validation and one hot encoding used in lessons and projects are described, but only briefly. With all of this in mind, this is an entry level course on the Keras library. What are you going to Learn? How to develop and evaluate neural network models end-to-end.How to use more advanced techniques required for developing state-of-the-art deep learning models.How to build larger models for image and text data.How to use advanced image augmentation techniques in order to lift model performance.How to get help with deep learning in Python.The anatomy of a Keras model. Evaluate the Performance of a deep learning Keras model.Build end-to end regression and classification models in Keras. How to use checkpointing to save the best model run. How to reduce overfitting With Dropout Regularization.How to enhance performance with Learning Rate Schedules. Work through a crash course on Convolutional Neural Networks. This course is a hands on-guide. It is a playbook and a workbook intended for you to learn by doing and then apply your new understanding to your own deep learning Keras models. To get the most out of the course, I would recommend working through all the examples in each tutorial. If you watch this course like a movie you'll get little out of it. In the applied space machine learning is programming and programming is a hands on-sport. Thank you for your interest in Hands-On Keras for Machine Learning Engineers.Let's get started!
