Natural Language Generation with Python
1h 49mAdvanced2024-01-19
Authors
Janani Ravi
Certified Google Cloud Architect and Data Engineer
Course details
Natural language generation is one of the most popular subfields of AI, and it only stands to become more prominent over the next few years. In this course, software developer Janani Ravi introduces you to the core concepts and fundamental skills of natural language generation with the widely used, open-source programming language Python.
Explore the basics of natural language generation using SimpleNLG and deep learning tools such as transformer models and the Hugging Face Transformers library. Janani shows you how to fine-tune a model for natural language generation and configure decoding strategies for text generation. By the end of this course, you’ll be equipped with in-demand, cutting-edge skills to take your software development career to the next level.
Explore the basics of natural language generation using SimpleNLG and deep learning tools such as transformer models and the Hugging Face Transformers library. Janani shows you how to fine-tune a model for natural language generation and configure decoding strategies for text generation. By the end of this course, you’ll be equipped with in-demand, cutting-edge skills to take your software development career to the next level.
Skills covered
AdvancedPythonProgramming LanguagesOpen SourceSoftware Development
Concepts
0. Introduction
- 01 - Introducing natural language generation
1. Natural Language Generation Using Good Old AI
- 02 - Natural language generation using AI
- 03 - Introducing SimpleNLG
- 04 - Generating text with SimpleNLG
- 05 - Using SimpleNLG for basic financial analysis
2. Natural Language Generation Using Deep Learning
- 06 - Natural language generation using deep learning
- 07 - Understanding attention in language models
- 08 - Introducing transformer models
- 09 - The Hugging Face Transformers library
3. Fine-Tuning a Model for Natural Language Generation
- 10 - Exploring model and dataset for natural language generation
- 11 - Setting up Google Colab
- 12 - Loading and exploring training data
- 13 - Tokenization and text generation
- 14 - Computing ROUGE scores for generated text
- 15 - Training the T5 small model
- 16 - Visualizing training and validation losses
- 17 - Generating text and computing ROUGE scores
4. Configuring Decoding Strategies for Text Generation
- 18 - Understanding language generation models
- 19 - Decoding strategies for language generation
- 20 - Exploring the GPT2 transformer on Hugging Face
- 21 - Text generation using Greedy Search
- 22 - Text generation using Beam Search
- 23 - Text generation using sampling
- 24 - Constrained beam search
Conclusion
- 25 - References, summary, and next steps
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