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Udemy - Natural Language Processing With Transformers in Python

Torrent: Udemy - Natural Language Processing With Transformers in Python
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Natural Language Processing With Transformers in Python
Learn next-generation NLP with transformers for sentiment analysis, Q&A, similarity search, NER, and more



This course includes:
* 11.5 hours on-demand video




What you'll learn
* Industry standard NLP using transformer models
* Build full-stack question-answering transformer models
* Perform sentiment analysis with transformers models in PyTorch and TensorFlow
* Advanced search technologies like Elasticsearch and Facebook AI Similarity Search (FAISS)
* Create fine-tuned transformers models for specialized use-cases
* Measure performance of language models using advanced metrics like ROUGE
* Vector building techniques like BM25 or dense passage retrievers (DPR)
* An overview of recent developments in NLP
* Understand attention and other key components of transformers
* Learn about key transformers models such as BERT
* Preprocess text data for NLP
* Named entity recognition (NER) using spaCy and transformers
* Fine-tune language classification models


Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again.

In this course, we learn all you need to know to get started with building cutting-edge performance NLP applications using transformer models like Google AI's BERT, or Facebook AI's DPR.

We cover several key NLP frameworks including:

HuggingFace's Transformers

TensorFlow 2

PyTorch

spaCy

NLTK

Flair

And learn how to apply transformers to some of the most popular NLP use-cases:

Language classification/sentiment analysis

Named entity recognition (NER)

Question and Answering

Similarity/comparative learning

Throughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application.

All of this is supported by several other sections that encourage us to learn how to better design, implement, and measure the performance of our models, such as:

History of NLP and where transformers come from

Common preprocessing techniques for NLP

The theory behind transformers

How to fine-tune transformers

We cover all this and more, I look forward to seeing you in the course!

Downloads: 224
Category: Other/Tutorials
Size: 3.3 GB
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Added: 2021-06-19 11:04:28
Language: English
Peers: Seeders : 13 , Leechers : 10
Tags: courses tutorials udemy notmrME 
Release name: Udemy - Natural Language Processing With Transformers in Python
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