©Copyright 2022, 2023, Denis Rothman, Packt Publishing
Last updated: July 1,2023
Dolphin 🐬 Additional Bonus programs for OpenAI ChatGPT(GPT-3.5 legacy), ChatGPT Plus(GPT-3.5 default, GPT 3.5 default, and GPT-4).
API examples for GPT-3.5-turbo, GPT-4, DALL-E 2, Google Cloud AI Language, and Google Cloud AI Vision.
Disover HuggingGPT, Google Smart Compose, Google BARD, and Microsoft's New Bing .
Advanced prompt engineering with the ChatGPT API and the GPT-4 API.
Just look for the Dolphin 🐬 and enjoy your ride into the future of AI!
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Transformer models from BERT to GPT-4, environments from Hugging Face to OpenAI. Fine-tuning, training, and prompt engineering examples. A bonus section with ChatGPT, GPT-3.5-turbo, GPT-4, and DALL-E including jump starting GPT-4, speech-to-text, text-to-speech, text to image generation with DALL-E and more.
You can run these notebooks on cloud platforms like Google Colab or your local machine. Note that some chapters require a GPU to run in a reasonable amount of time, so we recommend one of the cloud platforms as they come pre-installed with CUDA.
To run these notebooks on a cloud platform, just click on one of the badges in the table below or run them on your environment.
Bonus | Colab | Kaggle | Gradient | SageMaker Studio Lab |
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🐬Explore and compare ChatGPT, GPT-4 and GPT-3 models | ||||
Exploring_GPT_4_API | ||||
🐬Create a ChatGPT XAI function that explains ChatGPT and an XAI SHAP function | ||||
XAI_by_ChatGPT_for_ChatGPT | ||||
🐬Go back to the origins with GPT-2 and ChatGPT | ||||
GPT_2_and_ChatGPT_the_Origins | ||||
🐬ChatGPT or davinin_instruct? What is best for your project? | ||||
ChatGPT_as_a_Cobot_ChatGPT_versus_davinci_instruct.ipynb | ||||
🐬AI Language Model Comparison -Explore various AI language models and their capabilities through this comprehensive notebook. -Dive into different APIs and functionalities, such as sentiment analysis, entity recognition, syntax analysis, content classification, and AI vision. -Discover and compare the offerings of Google Cloud AI Language,Google Cloud AI Vision, OpenAI GPT-4, Google Bard, Microsoft New Bing, ChatGPT Plus-GPT-4, Hugging Face, HuggingGPT, and Google Smart Compose. |
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Exploring_and_Comparing_Advanced_AI_Technologies.ipynb |
Implement models, such as BERT, Reformer, and T5, that outperform classical language models
Compare NLP applications using GPT-3, GPT-2, and other transformers
Analyze advanced use cases, including polysemy, cross-lingual learning, and computer vision. A GitHub BONUS directory with SOA ChatGPT, GPT-3.5-turbo, GPT-4, and DALL-E notebooks.
Transformers are a game-changer for natural language understanding (NLU) and have become one of the pillars of artificial intelligence.
Transformers for Natural Language Processing, 2nd Edition, investigates deep learning for machine translations, language modeling, question-answering, and many more NLP domains with transformers.
An Industry 4.0 AI specialist needs to be adaptable; knowing just one NLP platform is not enough anymore. Different platforms have different benefits depending on the application, whether it's cost, flexibility, ease of implementation, results, or performance. In this book, we analyze numerous use cases with Hugging Face, Google Trax, OpenAI, and AllenNLP.
This book takes transformers' capabilities further by combining multiple NLP techniques, such as sentiment analysis, named entity recognition, and semantic role labeling, to analyze complex use cases, such as dissecting fake news on Twitter. Also, see how transformers can create code using just a brief description.
By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models to various datasets.
Discover new ways of performing NLP techniques with the latest pretrained transformers
Grasp the workings of the original Transformer, GPT-3, BERT, T5, DeBERTa, and Reformer
Create language understanding Python programs using concepts that outperform classical deep learning models
Apply Python, TensorFlow, and PyTorch programs to sentiment analysis, text summarization, speech recognition, machine translations, and more
Measure the productivity of key transformers to define their scope, potential, and limits in production
If you want to learn about and apply transformers to your natural language (and image) data, this book is for you.
A good understanding of NLP, Python, and deep learning is required to benefit most from this book. Many platforms covered in this book provide interactive user interfaces, which allow readers with a general interest in NLP and AI to follow several chapters of this book.
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
12.Detecting Customer Emotions to Make Predictions
13.Analyzing Fake News with Transformers
14.Interpreting Black Box Transformer Models
15.From NLP to Task-Agnostic Transformer Models
16.The Emergence of Transformer-Driven Copilots
17.The Consolidation of Suprahuman Transformers with OpenAI's ChatGPT and GPT-4
Appendix I: Terminology of Transformer Models
Appendix II: Hardware Constraints for Transformer Models
And more!