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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-pytorch-for-natural-language-processing/
课程评论：没有评论
课程名称：学习 PyTorch 进行自然语言处理 课程概述：PyTorch 是一个以 Python 编写的深度学习框架，因其易用性和动态计算图而受到数据科学专业人士的关注。该课程采取实践导向，充满现实案例，帮助学员使用 PyTorch 创建自己的应用程序！学员将通过 PyTorch 代码学习最常用的深度学习模型、技术和算法，并接触到 Transfer Learning（迁移学习）、自然语言处理和生成对抗网络的高级概念。进一步地，您将构建现实世界的 NLP 应用程序，如情感分析器和高级神经翻译机。 课程内容和概述：该培训项目包含两个完整的课程，精心挑选以提供最全面的培训体验。第一个课程是《七天学会 PyTorch 深度学习》，旨在为希望快速入门 PyTorch 的学员提供指导。课程将介绍最常用的深度学习模型、技术和算法，突破深度学习复杂性的迷思，展示只需正确的工具和简单直观的核心概念说明，深度学习也能像其他应用开发技术一样容易。课程将带您从基础深入到高级概念，如迁移学习、自然语言处理及生成对抗网络的实现。完成课程后，您将能够使用 PyTorch 构建深度学习应用。 第二个课程是《动手实践自然语言处理与 PyTorch》，在课程中学员将构建两个完整的现实世界 NLP 应用程序。第一个应用程序是情感分析器，用于分析数据以判断某个电影的评论是正面还是负面。随后，您将创建一个高级神经翻译机，这是一个语音翻译引擎，利用序列到序列模型的速度和灵活性，将给定文本翻译成不同语言。完成该课程后，您将能够使用 PyTorch 的深度学习能力构建自己的现实世界 NLP 模型。 关于作者：Will Ballard 是 GLG 的首席技术官，负责工程和 IT。他负责设计和操作大型数据中心，为包括 Gannett、Hearst Magazines、NFL、NPR、《华盛顿邮报》和 Whole Foods 等客户提供网站服务。他还在 NetSolve（现为 Cisco）、NetSpend 和 Works（现为美国银行）的软件开发中担任领导角色。Jibin Mathew 是一位科技创业者、人工智能爱好者和积极的研究者。他在人工智能领域专注了多年，担任软件解决方案架构师，并建筑了各种人工智能解决方案，涵盖计算机视觉、自然语言处理和数据科学，推动计算性能与模型准确性的极限。
PyTorch: written in Python, 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. PyTorch is a Deep Learning framework that is a boon for researchers and data scientists.This course takes a practical approach and is filled with real-world examples to help you create your own application using PyTorch! Learn the most commonly used Deep Learning models, techniques, and algorithms through PyTorch code. Get yourself acquainted with the advanced concepts such as Transfer Learning, Natural Language Processing and implementation of Generative Adversarial Networks. Moving further you will build real-world NLP applications such as Sentiment Analyzer & advanced Neural Translation Machine.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, PyTorch Deep Learning in 7 Days is for those who are in a hurry to get started with PyTorch. You will be introduced to the most commonly used Deep Learning models, techniques, and algorithms through PyTorch code. This course is an attempt to break the myth that Deep Learning is complicated and show you that with the right choice of tools combined with a simple and intuitive explanation of core concepts, Deep Learning is as accessible as any other application development technologies out there. It's a journey from diving deep into the fundamentals to getting acquainted with the advanced concepts such as Transfer Learning, Natural Language Processing and implementation of Generative Adversarial Networks. By the end of the course, you will be able to build Deep Learning applications with PyTorch.The second course, Hands-On Natural Language Processing with Pytorch you will build two complete real-world NLP applications throughout the course. The first application is a Sentiment Analyzer that analyzes data to determine whether a review is positive or negative towards a particular movie. You will then create an advanced Neural Translation Machine that is a speech translation engine, using Sequence to Sequence models with the speed and flexibility of PyTorch to translate given text into different languages. By the end of the course, you will have the skills to build your own real-world NLP models using PyTorch's Deep Learning capabilities.About the Authors:Will Ballard is the chief technology officer at GLG, responsible for engineering and IT. He was also responsible for the design and operation of large data centres that helped run site services for customers including Gannett, Hearst Magazines, NFL, NPR, The Washington Post, and Whole Foods. He has also held leadership roles in software development at NetSolve (now Cisco), NetSpend, and Works (now Bank of America).Jibin Mathew is a Tech-Entrepreneur, Artificial Intelligence enthusiast and an active researcher. He has spent several years as a Software Solutions Architect, with a focus on Artificial Intelligence for the past 5 years. He has architected and built various solutions in Artificial Intelligence which includes solutions in Computer Vision, Natural Language Processing/Understanding and Data sciences, pushing the limits of computational performance and model accuracies. He is well versed with concepts in Machine learning and Deep learning and serves as a consultant for clients from Retail, Environment, Finance and Health care.
