Reiter
Python Notebook and Slides
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.
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.
In this tutorial, we will walk you through the steps to implement the BERT model from scratch in PyTorch.
The tutorial will begin with an overview of the BERT model architecture and the pre-training process. We will then discuss the process of fine-tuning BERT for downstream tasks and walk through an example of fine-tuning BERT for text classification.
We will then move on to the implementation of the BERT model in PyTorch, starting with the construction of the attention mechanism and the transformer encoder blocks.
Throughout the tutorial, we will provide detailed explanations of each step and code snippets to help you follow along.
The tutorial will begin with an overview of the BERT model architecture and the pre-training process. We will then discuss the process of fine-tuning BERT for downstream tasks and walk through an example of fine-tuning BERT for text classification.
We will then move on to the implementation of the BERT model in PyTorch, starting with the construction of the attention mechanism and the transformer encoder blocks.
Throughout the tutorial, we will provide detailed explanations of each step and code snippets to help you follow along.