Transformers for Natural Language Processing by Denis Rothman
Book Summary: Transformers for Natural Language Processing (2nd Edition) by Denis Rothman
Explore the world of transformers in AI with this comprehensive book! From OpenAI’s GPT-3 to Hugging Face models, learn how to build, train, and fine-tune deep neural network architectures for NLP tasks using Python.
Key Features:
- Enhance productivity with OpenAI’s ChatGPT and GPT-4
- Pretrain a BERT-based model using Hugging Face
- Fine-tune powerful transformer models, including GPT-3
- Dive into machine translations, speech-to-text, text-to-speech, and more
- Address complex language problems and explore cutting-edge platforms
Book Description:
Transformers are reshaping AI, and this book is your guide! Discover strengths of different models, pretrain a RoBERTa model, and fine-tune GPT-3. Explore NLP tasks, machine translations, and the intersection of transformers with computer vision tasks.
What You Will Learn:
- Investigate complex language problems
- Compare GPT-3 with T5, GPT-2, and BERT-based transformers
- Perform sentiment analysis, text summarization, and more
- Understand ViT and CLIP for image processing
- Master advanced prompt engineering for ChatGPT and GPT-4
Who Is This Book For:
Perfect for those wanting to apply transformers to NLP and image data. Requires a good understanding of Python, deep learning, and basic NLP knowledge. Interactive interfaces make it accessible for general interest in AI. Direct access to an AI/ML community is provided for guidance.
Table of Contents:
1. What are Transformers?
2. Getting Started with the Architecture of the Transformer Model
3. Fine-Tuning BERT Models
4. Pretraining a RoBERTa Model from Scratch
5. Downstream NLP Tasks with Transformers
6. Machine Translation with the Transformer
7. The Rise of Suprahuman Transformers with GPT-3 Engines
8. Applying Transformers to Legal and Financial Documents for AI Text Summarization
9. Matching Tokenizers and Datasets
10. Semantic Role Labeling with BERT-Based Transformers
11. Let Your Data Do the Talking: Story, Questions, and Answers
Get your copy now and embark on a transformative journey into the world of AI and transformers!
Book Summary: Transformers for Natural Language Processing (2nd Edition) by Denis Rothman
Explore the world of transformers in AI with this comprehensive book! From OpenAI’s GPT-3 to Hugging Face models, learn how to build, train, and fine-tune deep neural network architectures for NLP tasks using Python.
Key Features:
– Enhance productivity with OpenAI’s ChatGPT and GPT-4
– Pretrain a BERT-based model using Hugging Face
– Fine-tune powerful transformer models, including GPT-3
– Dive into machine translations, speech-to-text, text-to-speech, and more
– Address complex language problems and explore cutting-edge platforms
Book Description:
Transformers are reshaping AI, and this book is your guide! Discover strengths of different models, pretrain a RoBERTa model, and fine-tune GPT-3. Explore NLP tasks, machine translations, and the intersection of transformers with computer vision tasks.
What You Will Learn:
– Investigate complex language problems
– Compare GPT-3 with T5, GPT-2, and BERT-based transformers
– Perform sentiment analysis, text summarization, and more
– Understand ViT and CLIP for image processing
– Master advanced prompt engineering for ChatGPT and GPT-4
Who Is This Book For:
Perfect for those wanting to apply transformers to NLP and image data. Requires a good understanding of Python, deep learning, and basic NLP knowledge. Interactive interfaces make it accessible for general interest in AI. Direct access to an AI/ML community is provided for guidance.
Table of Contents:
1. What are Transformers?
2. Getting Started with the Architecture of the Transformer Model
3. Fine-Tuning BERT Models
4. Pretraining a RoBERTa Model from Scratch
5. Downstream NLP Tasks with Transformers
6. Machine Translation with the Transformer
7. The Rise of Suprahuman Transformers with GPT-3 Engines
8. Applying Transformers to Legal and Financial Documents for AI Text Summarization
9. Matching Tokenizers and Datasets
10. Semantic Role Labeling with BERT-Based Transformers
11. Let Your Data Do the Talking: Story, Questions, and Answers
Get your copy now and embark on a transformative journey into the world of AI and transformers!
