|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/hands-on-natural-language-processing-with-pytorch/
课程评论：没有评论
课程名称：利用 PyTorch 进行实用自然语言处理 课程概述：本课程的主要目标是培训学员使用 Deep Learning 和 PyTorch 执行复杂的自然语言处理（NLP）任务，构建智能语言应用程序。在课程中，学员将完成两个实际的 NLP 应用程序的构建。第一个应用程序是情感分析器，能够分析数据以判断影评是对特定电影的积极还是消极评价。接着，学员将创建一个先进的神经翻译机，这是一个语音翻译引擎，使用序列到序列模型，利用 PyTorch 的快速性和灵活性将给定文本翻译成不同语言。到课程结束时，学员将掌握利用 PyTorch 的深度学习能力构建自己的实际 NLP 模型的技能。课程使用的技术包括 Python 3.6、PyTorch 1.0、NLTK 3.3.0 和 Spacy 2.0，虽然不是最新版本，但为 PyTorch 的传统用户提供了相关且具有信息价值的内容。 作者介绍：Jibin Mathew 是一位技术企业家、人工智能爱好者兼活跃研究者。在过去的五年中，他作为软件解决方案架构师专注于人工智能领域，并 architected 和构建了多个人工智能解决方案，包括计算机视觉、自然语言处理/理解和数据科学，推动了计算性能和模型准确性的极限。他对机器学习和深度学习的概念非常熟悉，并为零售、环境、金融和医疗等领域的客户提供咨询服务。
The main goal of this course is to train you to perform complex NLP tasks (and build intelligent language applications) using Deep Learning 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.This course uses Python 3.6, Pytorch 1.0, NLTK 3.3.0, and Spacy 2.0 , while not the latest version available, it provides relevant and informative content for legacy users of PyTorch.About the Author: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 a consultant for clients from Retail, Environment, Finance and Health care.
