Applied Natural Language Processing in the Enterprise

Reading Journey

Started: 2022-10-31T20:54:43Z
Ended: 2022-11-06T20:03:02Z
Total Time Read: 3hrs 47mins 51secs

Code

%run _help_reading.py
import pandas as pd

df = pd.read_csv(
  'https://github.com/MrGeislinger/victorsothervector/raw/main/'
  'data/reading/all_reading-clean.csv'
)

book_name = """Applied Natural Language Processing in the Enterprise"""
one_title = one_title_data(df, book_name)
one_title_summary = get_summary_by_day(one_title)
generate_plot(one_title_summary, book_name);

/Users/victor/Developer/Workspace/victorsothervector/books/_help_reading.py:78: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. Otherwise, ticks may be mislabeled.
  ax.set_xticklabels(

Figure 1: Reading done for Applied Natural Language Processing in the Enterprise

title: “Applied Natural Language Processing in the Enterprise” book: title: “Applied Natural Language Processing in the Enterprise” format: html: code-fold: true creator: “@victorgeislinger” —

Thoughts on Applied Natural Language Processing in the Enterprise

Overview

NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, you’ll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Build core parts of the NLP pipeline–including tokenizers, embeddings, and language models–from scratch using Python and PyTorch Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production

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