TensorFlow: Working with NLP

Contents

• Introduction Introduction

  • Why TensorFlow?

    46s
  • What you should know

    31s
  • What is TensorFlow?

    2m 40s
• 1. NLP and Transformers 1. NLP and Transformers

  • What is NLP?

    2m 45s
  • Transformers, their use, and history

    2m 9s
  • Transformers for NLP

    3m 7s
  • Challenge: NLP model size

    45s
  • Solution: NLP model size

    1m 34s
• 2. BERT and Transfer Learning 2. BERT and Transfer Learning

  • Bias in BERT and GPT

    3m 20s
  • How was BERT trained?

    2m 5s
  • Transfer learning

    2m 50s
• 3. Transformers and BERT 3. Transformers and BERT

  • Transformer: Architecture overview

    1m 31s
  • BERT model and tokenization

    3m 35s
  • Tokenizers

    2m 16s
  • Self-attention

    1m 59s
  • Multi-head attention and feedforward network

    51s
  • Fine-tuning BERT

    7m 58s
• Conclusion Conclusion

  • Next steps

    47s