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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-with-pytorch/
课程评论：没有评论
课程名称：使用PyTorch进行深度学习 课程概述：本视频课程将带您快速入门最前沿的深度学习库之一：PyTorch。PyTorch采用Python语言编写，因其易用性而受到所有数据科学专业人士的关注，并且支持动态计算图。在本课程中，您将学习如何使用卷积神经网络（CNN）处理图像等空间数据，以及使用递归神经网络（RNN）处理文本等序列数据。您还将探索如何利用无标签数据，通过自编码器实现有效的数据处理。此外，您将使用强化学习训练神经网络，使其能够独立学习如何保持平衡杆的平衡。在整个学习过程中，您将实现PyTorch框架的多种机制来完成这些任务。 课程结束时，您将对使用的算法和技术有深刻的理解，并掌握PyTorch的运作方式，从而能够在日常机器学习问题中应用该工具。尽管本课程使用的是Python 3.6和PyTorch 0.3，虽然不是最新版本，但为Python和PyTorch的传统用户提供了相关且有价值的内容。 关于作者：Anand Saha是一位拥有15年企业产品和服务开发经验的软件专业人士。2007年，他曾在TATA Communications工作，利用机器学习预测通话模式。在Symantec和Veritas期间，他参与了多项企业备份产品的功能开发，该产品被财富500强公司使用。他在学习深度学习的过程中，参加了Coursera和Udacity的MOOC课程。Anand对深度学习及其应用充满热情，以至于在2017年初辞去了Veritas的职位，全职专注于深度学习实践。他构建了从空中图像中检测和计数濒危物种的管道，训练机械臂进行物品的抓取与放置，并实现了NIPS论文中的模型。他的兴趣领域包括计算机视觉和模型优化。
This video course will get you up-and-running with one of the most cutting-edge deep learning libraries: PyTorch. Written in Python, PyTorch is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs.In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you will implement various mechanisms of the PyTorch framework to do these tasks.By the end of the video course, you will have developed a good understanding of, and feeling for, the algorithms and techniques used. You'll have a good knowledge of how PyTorch works and how you can use it in to solve your daily machine learning problems.This course uses Python 3.6, and PyTorch 0.3, while not the latest version available, it provides relevant and informative content for legacy users of Python, and PyTorch.About the AuthorAnand Saha is a software professional with 15 years' experience in developing enterprise products and services. Back in 2007, he worked with machine learning to predict call patterns at TATA Communications. At Symantec and Veritas, he worked on various features of an enterprise backup product used by Fortune 500 companies. Along the way he nurtured his interests in Deep Learning by attending Coursera and Udacity MOOCs.He is passionate about Deep Learning and its applications; so much so that he quit Veritas at the beginning of 2017 to focus full time on Deep Learning practices. Anand built pipelines to detect and count endangered species from aerial images, trained a robotic arm to pick and place objects, and implemented NIPS papers. His interests lie in computer vision and model optimization.
