Question Answering with Deep Learning

prerequisites
intermediate Python • beginner scikit-learn and PyTorch • basics of deep learning • basics of NLP
skills learned
deep learning for NLP • information retrieval • extracting text from PDFs • working with transformers and auto-encoders • working with word and paragraph embeddings
Matteus Tanha
4 weeks · 7-10 hours per week · INTERMEDIATE
filed under

placing your order...

Don't refresh or navigate away from the page.
liveProject liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $34.99 $49.99 you save: $15 (30%)
Question Answering with Deep Learning (liveProject) added to cart
continue shopping
go to cart

Look inside
In this liveProject, you’ll step into the role of a data scientist working for an investment firm. Your company wants to make sure their investments meet European Union guidelines for environmental sustainability. That’s where you come in.

The EU taxonomy for sustainable finance is big, complex, and confusing. Your bosses need a program that saves them from searching through hundreds of pages whenever they have a query. You’ve been tasked with building a machine learning model that can pose certain questions to the EU guidelines, and return reliable answers.

Your challenges will include extracting text data from the EU taxonomy document, and matching environment questions with the corresponding paragraph in the guidelines. You’ll then set up a pretrained transformer Question-Answering model, evaluate its performance, and combine it with your question-paragraph model for an end-to-end solution. When you’re done, you’ll have an interface into which you can type a sustainable finance question and receive the correct answer from the EU guidelines.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Matteus Tanha
Matteus Tanha is the Head of Machine Learning at Alpha Quants, a consulting firm with a focus on Natural Language Processing solutions. He earned his Ph.D. specializing in machine learning applied to quantum chemistry from Carnegie Mellon University. For the past 6 years, Matteus has been developing various natural language processing solutions for companies across the sectors of finance, academia, and media.

prerequisites

This liveProject is for intermediate Python programmers and who already know the basics of data science and Machine Learning. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Intermediate Python
  • Basics of pandas
  • Basics of NumPy
  • Basics of scikit-learn
TECHNIQUES
  • Basics of data science
  • Basics of machine learning

you will learn

In this liveProject, you’ll get to grips with fundamentals of Information Retrieval and Natural Language Processing that are the cornerstone of data and deep learning projects.

  • Deep learning with Pytorch and Spacy
  • Extracting text from PDFs
  • Evaluating machine learning models
  • Loading and working with pretrained models
  • Transformers and auto-encoders
  • Word and paragraph embeddings

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
RECENTLY VIEWED