Natural Language Processing with PyTorch

سرفصل های دوره

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.

1. Course Overview

1. Course Overview

2. Implementing Recurrent Neural Networks (RNNs) in PyTorch

1. Version Check

2. Module Overview

3. Prerequisites and Course Outline

4. RNNs for Natural Language Processing

5. Recurrent Neurons

6. Back Propagation through Time

7. Coping with Vanishing and Exploding Gradients

8. Long Memory Cells

9. Module Summary

3. Performing Binary Text Classification Using Words

01. Module Overview

02. Word Embeddings to Represent Text Data

03. Introducing torchtext to Process Text Data

04. Feeding Text Data into RNNs

05. Setup and Data Cleaning

06. Using Torchtext to Process Text Data

07. Designing an RNN for Binary Text Classification

08. Training the RNN

09. Using LSTM Cells and Dropout

10. Module Summary

4. Performing Multi-class Text Classification Using Characters

1. Module Overview

2. Language Prediction Based on Names

3. Loading and Cleaning Data

4. Helper Functions to One Hot Encode Names

5. Designing an RNN for Multiclass Text Classification

6. Predicting Language from Names

7. Module Summary

5. Performing Sentiment Analysis Using Word Embeddings

01. Module Overview

02. Numeric Representations of Words

03. Word Embeddings Capture Context and Meaning

04. Generating Analogies Using GloVe Embeddings

05. Multilayer RNNs

06. Bidirectional RNNs

07. Data Cleaning and Preparation

08. Designing a Multilayer Bidirectional RNN

09. Performing Sentiment Analysis Using an RNN

10. Module Summary

6. Performing Language Translation Using Sequence-to-Sequence Models

01. Module Overview

02. Using Sequences and Vectors with RNNs

03. Language Translation Using Encoders and Decoders

04. Representing Input and Target Sentences

05. Teacher Forcing

06. Setting up Helper Functions for Language Translation

07. Preparing Sentence Pairs

08. Designing the Encoder and Decoder

09. Training the Sequence-2-Sequence Model Using Teacher Forcing

10. Translating Sentences

11. Summary and Further Study

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تولید کننده: PluralSight