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You’re a data scientist at Finative, an environmental, social, and governance (ESG) analytics company that analyzes a high volume of data using advanced natural language processing (NLP) techniques in order to provide its clients insights for sustainable investing. Recently, your CEO has decided that Finative should increase its own financial sustainability. Your task is to classify sustainability reports of a publicly traded company in an efficient and sustainable way.
You’ll learn the fundamental mathematics—including backpropagation, matrix multiplication, and attention mechanisms—of Transformers, empowering you to optimize your model’s performance, improve its efficiency, and handle undesirable model predictions. You’ll use Python’s pdfplumber library to extract text from a sustainability report for quick delivery to your CEO. To further increase efficiency, you’ll save training time by using a language model that’s been pre-trained with ESG data to build a pipeline for the model and classify the sustainability report.
This liveProject is for ML engineers, intermediate-level Python programmers, and early-stage data scientists who are familiar with the basics of linear algebra. To begin these liveProjects you’ll need to be familiar with the following:TOOLS
In this liveProject, you’ll learn the fundamental mathematics of backpropagation, and you’ll get an understanding of the inner workings of a Transformer-based deep learning network in order to effectively and efficiently classify documents.
geekle is based on a wordle clone.