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Oreilly – Transfer Learning for Natural Language Processing 2021-8

Updated August 10, 2026 1.1 GB
Oreilly – Transfer Learning for Natural Language Processing 2021-8

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Description

Transfer Learning for Natural Language Processing. In this course, you will learn how to adapt pre-trained machine learning models to solve specialized natural language processing problems using transfer learning. Training deep natural language processing models from scratch is expensive, time-consuming, and requires huge amounts of data. In this course, DARPA researcher Paul Azur introduces advanced transfer learning techniques that you can use to apply customizable pre-trained models to your natural language processing architectures. You will learn how to use transfer learning to achieve superior language understanding results, even with limited labeled data. Most importantly, you will save training time and computational costs.

Build custom natural language processing models in record time, even with limited datasets, using transfer learning! Transfer learning is a machine learning technique for adapting pre-trained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, helping to improve machine translation, business analytics, and natural language generation.

What you will learn:

  • Fine-tuning pre-trained models with new domain data
  • Choosing the right model to reduce resource use
  • Transfer learning for neural network architectures
  • Text generation with pre-trained transformers
  • Cross-Language Learning Transfer with BERT
  • Fundamentals of Exploring Academic Literature Natural Language Processing

This course is suitable for people who:

  • Machine learning engineers and data scientists are experienced in natural language processing.

Course details

  • Publisher: Oreilly
  • Instructor: Paul Azunre
  • Training level: Beginner to advanced
  • Training duration: 6 hours and 48 minutes

Course headings

  • Part 1. Introduction and overview
  • Chapter 1. What is transfer learning?
  • Chapter 2. Getting started with baselines: Data preprocessing
  • Chapter 3. Getting started with baselines: Benchmarking and optimization
  • Part 2. Shallow transfer learning and deep transfer learning with recurrent neural networks (RNNs)
  • Chapter 4. Shallow transfer learning for NLP
  • Chapter 5. Preprocessing data for recurrent neural network deep transfer learning experiments
  • Chapter 6. Deep transfer learning for NLP with recurrent neural networks
  • Chapter 7. Deep transfer learning for NLP with the transformer and GPT
  • Chapter 8. Deep transfer learning for NLP with BERT and multilingual BERT
  • Chapter 9. ULMFiT and knowledge distillation adaptation strategies
  • Chapter 10. ALBERT, adapters, and multitask adaptation strategies
  • Chapter 11. Conclusions
  • Appendix A. Kaggle primer
  • Appendix B. Introduction to fundamental deep learning tools

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Transfer Learning for Natural Language Processing

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Quality: 720p

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Download Part 1 – 1 GB

Download Part 2 – 148 MB

File(s) password: www.downloadly.ir

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1.1 GB

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