Udemy - Natural Language Processing With Transformers in Python

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size3.3 GB
  • Uploaded BynotmrME
  • Downloads224
  • Last checkedJun. 23rd '21
  • Date uploadedJun. 19th '21
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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!

Files:

Natural Language Processing With Transformers in Python 07 Long Text Classification With BERT
  • 001 Classification of Long Text Using Windows.mp4 (116.1 MB)
  • 002 Window Method in PyTorch.mp4 (84.9 MB)
  • external-assets-links.txt (0.4 KB)
01 Introduction
  • 001 Introduction.mp4 (9.2 MB)
  • 002 Course Overview.mp4 (34.4 MB)
  • 003 Environment Setup.mp4 (37.3 MB)
  • 004 Alternative Setup.html (2.8 KB)
  • 005 CUDA Setup.mp4 (23.7 MB)
  • external-assets-links.txt (0.4 KB)
02 NLP and Transformers
  • 001 The Three Eras of AI.mp4 (22.2 MB)
  • 002 Pros and Cons of Neural AI.mp4 (32.8 MB)
  • 003 Word Vectors.mp4 (21.7 MB)
  • 004 Recurrent Neural Networks.mp4 (17.1 MB)
  • 005 Long Short-Term Memory.mp4 (6.3 MB)
  • 006 Encoder-Decoder Attention.mp4 (25.2 MB)
  • 007 Self-Attention.mp4 (20.8 MB)
  • 008 Multi-head Attention.mp4 (13.3 MB)
  • 009 Positional Encoding.mp4 (55.5 MB)
  • 010 Transformer Heads.mp4 (39.8 MB)
  • external-assets-links.txt (0.4 KB)
03 Preprocessing for NLP
  • 001 Stopwords.mp4 (23.0 MB)
  • 002 Tokens Introduction.mp4 (24.0 MB)
  • 003 Model-Specific Special Tokens.mp4 (18.9 MB)
  • 004 Stemming.mp4 (17.2 MB)
  • 005 Lemmatization.mp4 (10.6 MB)
  • 006 Unicode Normalization - Canonical and Compatibility Equivalence.mp4 (17.0 MB)
  • 007 Unicode Normalization - Composition and Decomposition.mp4 (20.3 MB)
  • 008 Unicode Normalization - NFD and NFC.mp4 (20.0 MB)
  • 009 Unicode Normalization - NFKD and NFKC.mp4 (30.4 MB)
  • external-assets-links.txt (1.0 KB)
04 Attention
  • 001 Attention Introduction.mp4 (15.8 MB)
  • 002 Alignment With Dot-Product.mp4 (49.1 MB)
  • 003 Dot-Product Attention.mp4 (29.0 MB)
  • 004 Self Attention.mp4 (28.4 MB)
  • 005 Bidirectional Attention.mp4 (10.8 MB)
  • 006 Multi-head and Scaled Dot-Product Attention.mp4 (33.8 MB)
  • external-assets-links.txt (0.7 KB)
05 Language Classification
  • 001 Introduction to Sentiment Analysis.mp4 (37.5 MB)
  • 002 Prebuilt Flair Models.mp4 (30.7 MB)
  • 003 Introduction to Sentiment Models With Transformers.mp4 (26.9 MB)
  • 004 Tokenization And Special Tokens For BERT.mp4 (55.4 MB)
  • 005 Making Predictions.mp4 (26.0 MB)
  • external-assets-links.txt (0.7 KB)
06 [Project] Sentiment Model With TensorFlow and Transformers
  • 001 Project Overview.mp4 (12.5 MB)
  • 002 Getting the Data (Kaggle API).mp4 (35.0 MB)
  • 003 Preprocessing.mp4 (62.5 MB)
  • 004 Building a Dataset.mp4 (22.6 MB)
  • 005 Dataset Shuffle, Batch, Split, and Save.mp4 (30.2 MB)
  • 006 Build and Save.mp4 (77.0 MB)
  • 007 Loading and Prediction.mp4 (56.8 MB)
  • external-assets-links.txt (0.8 KB)
  • Downloaded from 1337x.html (0.5 KB)
    • 08 Named Entity Recognition (NER)
    • 001 Introduction to spaCy.mp4 (51.6 MB)
    • 002 Extracting Entities.mp4 (33.5 MB)
    • 003 Authenticating With The Reddit API.mp4 (35.6 MB)
    • 004 Pulling Data With The Reddit API.mp4 (88.9 MB)
    • 005 Extracting ORGs From Reddit Data.mp4 (28.1 MB)
    • 006 Getting Entity Frequency.mp4 (18.4 MB)
    • 007 Entity Blacklist.mp4 (20.1 MB)
    • 008 NER With Sentiment.mp4 (99.9 MB)
    • 009 NER With roBERTa.mp4 (59.0 MB)
    • external-assets-links.txt (1.3 KB)
    09 Question and Answering
    • 001 Open Domain and Reading Comprehension.mp4 (16.1 MB)
    • 002 Retrievers, Readers, and Generators.mp4 (28.7 MB)
    • 003 Intro to SQuAD 2.0.mp4 (25.4 MB)
    • 004 Processing SQuAD Training Data.mp4 (38.4 MB)
    • 005 (Optional) Processing SQuAD Training Data with Match-Case.mp4 (30.1 MB)
    • 006 Our First Q&A Model.mp4 (45.7 MB)
    • external-assets-links.txt (0.9 KB)
    10 Metrics For Language
    • 001 Q&A Performance With Exact Match (EM).mp4 (18.2 MB)
    • 002 ROUGE in Python.mp4 (21.7 MB)
    • 003 Applying ROUGE to Q&A.mp4 (33.9 MB)
    • 004 Recall, Precision and F1.mp4 (21.0 MB)
    • 005 Longest Common Subsequence (LCS).mp4 (15.0 MB)
    • 006 Q&A Performance With ROUGE.mp4 (18.7 MB)
    • external-assets-links.txt (0.7 KB)
    11 Reader-Retriever QA With Haystack
    • 001 Intro to Retriever-Reader and Haystack.mp4 (13.9 MB)
    • 002 What is Elasticsearch_.mp4 (23.5 MB)
    • 003 Elasticsearch Setup (Windows).mp4 (20.9 MB)
    • 004 Elasticsearch Setup (Linux).mp4 (20.2 MB)
    • 005 Elasticsearch in Haystack.mp4 (39.0 MB)
    • 006 Sparse Retrievers.mp4 (20.4 MB)
    • 007 Cleaning the Index.mp4 (26.4 MB)
    • 008 Implementing a BM25 Retriever.mp4 (12.5 MB)
    • 009 What is FAISS_.mp4 (42.9 MB)
    • 010 FAISS in Haystack.mp4 (68.1 MB)
    • 011 What is DPR_.mp4 (29.7 MB)
    • 012 The DPR Architecture.mp4 (14.3 MB)
    • 013 Retriever-Reader Stack.mp4 (75.3 MB)
    • external-assets-links.txt (1.8 KB)
    12 [Project] Open-Domain QA
    • 001 ODQA Stack Structure.mp4 (6.2 MB)
    • 002 Creating the Database.mp4 (42.4 MB)
    • 003 Building the Haystack Pipeline.mp4 (55.8 MB)
    • external-assets-links.txt (0.4 KB)
    13 Similarity
    • 001 Introduction to Similarity.mp4 (28.2 MB)
    • 002 Extracting The Last Hidden State Tensor.mp4 (29.7 MB)
    • 003 Sentence Vectors With Mean Pooling.mp4 (32.1 MB)
    • 004 Using Cosine Similarity.mp4 (33.9 MB)
    • 005 Similarity With Sentence-Transformers.mp4 (23.0 MB)
    14 Fine-Tuning Transformer Models
    • 001 Visual Guide to BERT Pretraining.mp4 (28.6 MB)
    • 002 Introduction to BERT For Pretraining Code.mp4 (29.3 MB)
    • 003 BERT Pretraini

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