Introduction to PyTorch and Deep Learning III - Deep Learning for NLP (Introduction) - Lecture

Welcome to the Introduction to PyTorch and Deep Learning online class, which will teach you some basic topics in machine learning.

The lecture begins with an overview of NLP and its different components, including text preprocessing, tokenization, normalization, and feature extraction.

We then focus on transformer models and their impact on NLP, discussing how the transformer architecture enables more effective modeling of long-range dependencies in text. We also delve into some of the most popular transformer models, such as BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pretrained Transformer), and DALL-E 2, which are revolutionizing the field of NLP.

Throughout the video, we provide clear explanations of the different concepts and models in NLP, with illustrative examples and case studies of their applications in real-world scenarios. We will also discuss some of the key challenges and limitations of NLP, such as language ambiguity and the need for large amounts of annotated data.

0:00 Introduction and Outline
1:15 What is Natural Language Processing (NLP)?
18:26 Language Models
22:17 Transformers
55:10 GPT
58:16 ChatGPT

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