This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch.
From chatbots to machine-generated literature, some of the hottest applications of ML and AI these days are for data in textual form. In this course, Natural Language Processing with PyTorch, you will gain the ability to design and implement complex text processing models using PyTorch, which is fast emerging as a popular choice for building deep-learning models owing to its flexibility, ease-of-use, and built-in support for optimized hardware such as GPUs. First, you will learn how to leverage recurrent neural networks (RNNs) to capture sequential relationships within text data. Next, you will discover how to express text using word vector embeddings, a sophisticated form of encoding that is supported by out-of-the-box in PyTorch via the torchtext utility. Finally, you will explore how to build complex multi-level RNNs and bidirectional RNNs to capture both backward and forward relationships within data. You will round out the course by building sequence-to-sequence RNNs for language translation. When you are finished with this course, you will have the skills and knowledge to design and implement complex natural language processing models using sophisticated recurrent neural networks in PyTorch. Topics:
- Course Overview
- Implementing Recurrent Neural Networks (RNNs) in PyTorch
- Performing Binary Text Classification Using Words
- Performing Multi-class Text Classification Using Characters
- Performing Sentiment Analysis Using Word Embeddings
- Performing Language Translation Using Sequence-to-Sequence Models