Word2Vec、Glove、FastText、通用句子编码器、GRU、LSTM、Conv1D、Seq2Seq、机器翻译、聊天机器人等等

你会学到什么
使用深度学习模型升级自然语言处理知识

MP4 |视频:h264,1280×720 |音频:AAC,44.1 KHz
语言:英语+中英文字幕(云桥网络 机译)|大小解压后:4.12GB |时长:8h 18m

要求
机器学习,自然语言处理基础,线性代数,Python,张量流,Keras

自然语言处理是机器学习领域的一个热门话题。

本课程是使用深度学习方法的自然语言处理高级课程。

在开始本课程之前,请阅读第2课的指南,以获得本课程的最佳体验。

本课程从配置和安装所有需要的资源开始,包括安装Tensor Flow 1。X CPU/GPU,Cuda和Keras。如果你有图形处理器卡,你可以使用它来快速加速你的模型的训练过程。然而,如果你没有图形处理器卡,你可以使用谷歌可乐按照说明操作。
之后,我们将在第2章回顾深度学习的主要概念,以便将它们应用到自然语言处理领域,为您的主要章节提供坚实的背景。


在主要的第3章中,我们将研究主要的深度学习库和自然语言处理模型,例如

-单词嵌入,

– Word2Vec,

-手套,

-快速文本,

-通用句子编码器,

– RNN,

– GRU,

– LSTM,

-1D的回旋,

– Seq2Seq,

-记忆网络,

-和注意力机制。

本课程为您提供了许多不同数据集的示例,例如

-谷歌新闻,

– Yelp评论,

-亚马逊评论,

– IMDB审查,

——圣经文集等和不同的文本文集。

在第四章的期末考试中,你将通过实际应用来实践你的知识,例如

-多类情感分析,

-文本生成,

-机器翻译,

-开发聊天机器人等。

对于编码,我们将使用TensorFlow、Keras、Google Colab和许多Python库。

如果你需要自然语言处理或机器学习方面的背景知识,我向你推荐我的课程

用于机器学习和数据挖掘的Python

Python和自然语言处理

学生有机会通过问答论坛、电子邮件:machine.learning.eirl@gmail.com或推特:@ AILearningCQ获得讲师的反馈

这门课是给谁的
寻找使用深度学习方法的自然语言处理高级课程的专业人士

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.98 GB | Duration: 8h 18m

Word2Vec, Glove, FastText, Universal Sentence Encoder, GRU, LSTM, Conv1D, Seq2Seq, MachineTranslation, ChatBot, and more

What you’ll learn
Upgrade the knowledge of Natural Language Processing using Deep Learning models

Requirements
Machine Learning, NLP basics, Linear Algebra, Python, Tensor Flow, Keras
Description
Natural Language Processing (NLP) is a hot topic into Machine Learning field.

This course is an advanced course of NLP using Deep Learning approach.

Before starting this course please read the guidelines of the lesson 2 to have the best experience in this course.

This course starts with the configuration and the installation of all resources needed including the installation of Tensor Flow 1.X CPU/GPU, Cuda and Keras. You will be able to use your GPU card if you have one, to accelate fastly the training processes of your models. However if you dont have a GPU card you can follow the instructions using Google Colab.
After that we are going to review the main concepts of Deep Learning in the Chapter 2 for applying them into the Natural Language Processing field offering you a solid background for the main chapter.

In the main Chapter 3 we are going to study the main Deep Learning libraries and models for NLP such as

– Word Embeddings,

– Word2Vec,

– Glove,

– FastText,

– Universal Sentence Encoder,

– RNN,

– GRU,

– LSTM,

– Convolutions in 1D,

– Seq2Seq,

– Memory Networks,

– and the Attention mechanism.

This course offers you many examples, with different datasets suchs as

– Google News,

– Yelp comments,

– Amazon reviews,

– IMDB reviews,

– the Bible corpus, etc and different text corpus.

At the final in Chapter 4 you will put in practice your knowledge with practical applications such as

– Multiclass Sentiment Analysis,

– Text Generation,

– Machine Translation,

– Developing a ChatBot and more.

For coding we are going to use TensorFlow, Keras, Google Colab and many Python libraries.

If you need a previous background in Natural Language Processing or in Machine Learning I recommend you my courses

Python for Machine Learning and Data Mining or

Natural Language Processing with Python and NLTK

The student has the opportunity to get a feedback from the instructor through Q&A forums, by email: machine.learning.eirl@gmail.com or by Twitter: @AILearningCQ

Who this course is for
Professionals looking for an advanced course of Natural Language Processing using Deep Learning approach
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