Deep Learning for NLP - Part 6
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.38 GB | Duration: 2h 39m
What you'll learn
Deep Learning for Natural Language Processing
Popular Transformer encoder and decoder models
Multi-modal Transformer models
Large scale Transformer models
DL for NLP
Requirements
Basics of machine learning
Basic understanding of Transformer based models and word embeddings
Transformer Models like BERT and GPT
Description
This course is a part of "Deep Learning for NLP" Series. In this course, I will talk about various popular Transformer models beyond the ones I have already covered in the previous sessions in this series. Such Transformer models including encoder as well as decoder based models and differ in terms of various aspects like form of input, pretraining objectives, pretraining data, architecture variations, etc.
These Transformer models have been all proposed after 2019 and some of them are also from early 2021. Thus, as of Aug 2021, these models are very recent and state of the art across multiple NLP tasks.
The course consists of three main sections as follows.
