課程代碼

dlfornlp

課程時長

28 時間：同常來說包括休息是 4天

最低要求

An understanding of Python programming
An understanding of Python libraries in general

Audience

Programmers with interest in linguistics
Programmers who seek an understanding of NLP (Natural Language Processing)

概觀

NLP的深度學習使機器學習簡單到複雜的語言處理。當前可能的任務包括語言翻譯和照片的標題生成。 DL（深度學習）是ML（機器學習）的子集。 Python是一種流行的編程語言，包含用于NLP深度學習的庫。在這個有指導意義的現場培訓中，參與者將學習使用Python庫進行NLP（自然語言處理），因爲他們創建了一個處理一組圖片並生成字幕的應用程序。在培訓結束後，參與者將能夠：使用Python庫設計和編寫用于NLP的DL 創建讀取大量圖片並生成關鍵字的Python代碼創建Python代碼，從檢測到的關鍵字中生成字幕聽衆對語言學感興趣的程序員希望了解NLP（自然語言處理）的程序員課程的格式部分講座，部分討論，練習和沈重的練習

Machine Translated

課程簡介

Introduction to Deep Learning for NLP

Differentiating between the various types of DL models

Using pre-trained vs trained models

Using word embeddings and sentiment analysis to extract meaning from text

How Unsupervised Deep Learning works

Installing and Setting Up Python Deep Learning libraries

Using the Keras DL library on top of TensorFlow to allow Python to create captions

Working with Theano (numerical computation library) and TensorFlow (general and linguistics library) to use as extended DL libraries for the purpose of creating captions.

Using Keras on top of TensorFlow or Theano to quickly experiment on Deep Learning

Creating a simple Deep Learning application in TensorFlow to add captions to a collection of pictures

Troubleshooting

A word on other (specialized) DL frameworks

Deploying your DL application

Using GPUs to accelerate DL

Closing remarks