Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more
Build and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning models
Go through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machine
Test transformer models on advanced use cases
Description
The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers.
The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face.
The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification.
By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.
What you will learn
Use the latest pretrained transformer models
Grasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer models
Create language understanding Python programs using concepts that outperform classical deep learning models
Use a variety of NLP platforms, including Hugging Face, Trax, and AllenNLP
Apply Python, TensorFlow, and Keras 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
Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive natural language processing (NLP) chatbots applied as an automated language teacher for Moët et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an advanced planning and scheduling (APS) solution used worldwide.
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UK:
Economy: Delivery to most addresses in the U.K. within 7-9 business days. Shipments are not trackable
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Australia:
Economy: Can deliver to P. O. Boxes and private residences. Trackable service with delivery to addresses in Australia only. Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro Delivery time is up to 15 business days for remote areas of WA, NT & QLD.
Premium: Delivery to addresses in Australia only Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.
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Unfortunately, due to several restrictions, we are unable to ship to the following countries: