| 001 Attention Introduction.mp4 | 15.8 MB | ||
| 001 Classification of Long Text Using Windows.mp4 | 116.1 MB | ||
| 001 Intro to Retriever-Reader and Haystack.mp4 | 13.9 MB | ||
| 001 Introduction to Sentiment Analysis.mp4 | 37.5 MB | ||
| 001 Introduction to Similarity.mp4 | 28.2 MB | ||
| 001 Introduction to spaCy.mp4 | 51.6 MB | ||
| 001 Introduction.mp4 | 9.2 MB | ||
| 001 ODQA Stack Structure.mp4 | 6.2 MB | ||
| 001 Open Domain and Reading Comprehension.mp4 | 16.1 MB | ||
| 001 Project Overview.mp4 | 12.5 MB | ||
| 001 Q&A Performance With Exact Match (EM).mp4 | 18.2 MB | ||
| 001 Stopwords.mp4 | 23 MB | ||
| 001 The Three Eras of AI.mp4 | 22.2 MB | ||
| 001 Visual Guide to BERT Pretraining.mp4 | 28.6 MB | ||
| 002 Alignment With Dot-Product.mp4 | 49.1 MB | ||
| 002 Course Overview.mp4 | 34.4 MB | ||
| 002 Creating the Database.mp4 | 42.4 MB | ||
| 002 Extracting Entities.mp4 | 33.5 MB | ||
| 002 Extracting The Last Hidden State Tensor.mp4 | 29.7 MB | ||
| 002 Getting the Data (Kaggle API).mp4 | 35 MB | ||
| 002 Introduction to BERT For Pretraining Code.mp4 | 29.3 MB | ||
| 002 Prebuilt Flair Models.mp4 | 30.7 MB | ||
| 002 Pros and Cons of Neural AI.mp4 | 32.8 MB | ||
| 002 ROUGE in Python.mp4 | 21.7 MB | ||
| 002 Retrievers, Readers, and Generators.mp4 | 28.7 MB | ||
| 002 Tokens Introduction.mp4 | 24 MB | ||
| 002 What is Elasticsearch_.mp4 | 23.5 MB | ||
| 002 Window Method in PyTorch.mp4 | 84.9 MB | ||
| 003 Applying ROUGE to Q&A.mp4 | 33.9 MB | ||
| 003 Authenticating With The Reddit API.mp4 | 35.6 MB | ||
| 003 BERT Pretraining - Masked-Language Modeling (MLM).mp4 | 46.7 MB | ||
| 003 Building the Haystack Pipeline.mp4 | 55.8 MB | ||
| 003 Dot-Product Attention.mp4 | 29 MB | ||
| 003 Elasticsearch Setup (Windows).mp4 | 20.9 MB | ||
| 003 Environment Setup.mp4 | 37.3 MB | ||
| 003 Intro to SQuAD 2.0.mp4 | 25.4 MB | ||
| 003 Introduction to Sentiment Models With Transformers.mp4 | 26.9 MB | ||
| 003 Model-Specific Special Tokens.mp4 | 18.9 MB | ||
| 003 Preprocessing.mp4 | 62.5 MB | ||
| 003 Sentence Vectors With Mean Pooling.mp4 | 32.1 MB | ||
| 003 Word Vectors.mp4 | 21.7 MB | ||
| 004 Alternative Setup.html | 2.8 KB | ||
| 004 BERT Pretraining - Next Sentence Prediction (NSP).mp4 | 42.1 MB | ||
| 004 Building a Dataset.mp4 | 22.6 MB | ||
| 004 Elasticsearch Setup (Linux).mp4 | 20.2 MB | ||
| 004 Processing SQuAD Training Data.mp4 | 38.4 MB | ||
| 004 Pulling Data With The Reddit API.mp4 | 88.9 MB | ||
| 004 Recall, Precision and F1.mp4 | 21 MB | ||
| 004 Recurrent Neural Networks.mp4 | 17.1 MB | ||
| 004 Self Attention.mp4 | 28.4 MB | ||
| 004 Stemming.mp4 | 17.2 MB | ||
| 004 Tokenization And Special Tokens For BERT.mp4 | 55.4 MB | ||
| 004 Using Cosine Similarity.mp4 | 33.9 MB | ||
| 005 (Optional) Processing SQuAD Training Data with Match-Case.mp4 | 30.1 MB | ||
| 005 Bidirectional Attention.mp4 | 10.8 MB | ||
| 005 CUDA Setup.mp4 | 23.7 MB | ||
| 005 Dataset Shuffle, Batch, Split, and Save.mp4 | 30.2 MB | ||
| 005 Elasticsearch in Haystack.mp4 | 39 MB | ||
| 005 Extracting ORGs From Reddit Data.mp4 | 28.1 MB | ||
| 005 Lemmatization.mp4 | 10.6 MB | ||
| 005 Long Short-Term Memory.mp4 | 6.3 MB | ||
| 005 Longest Common Subsequence (LCS).mp4 | 15 MB | ||
| 005 Making Predictions.mp4 | 26 MB | ||
| 005 Similarity With Sentence-Transformers.mp4 | 23 MB | ||
| 005 The Logic of MLM.mp4 | 79.4 MB | ||
| 006 Build and Save.mp4 | 77 MB | ||
| 006 Encoder-Decoder Attention.mp4 | 25.2 MB | ||
| 006 Fine-tuning with MLM - Data Preparation.mp4 | 76.7 MB | ||
| 006 Getting Entity Frequency.mp4 | 18.4 MB | ||
| 006 Multi-head and Scaled Dot-Product Attention.mp4 | 33.8 MB | ||
| 006 Our First Q&A Model.mp4 | 45.7 MB | ||
| 006 Q&A Performance With ROUGE.mp4 | 18.7 MB | ||
| 006 Sparse Retrievers.mp4 | 20.4 MB | ||
| 006 Unicode Normalization - Canonical and Compatibility Equivalence.mp4 | 17 MB | ||
| 007 Cleaning the Index.mp4 | 26.4 MB | ||
| 007 Entity Blacklist.mp4 | 20.1 MB | ||
| 007 Fine-tuning with MLM - Training.mp4 | 69.7 MB | ||
| 007 Loading and Prediction.mp4 | 56.8 MB | ||
| 007 Self-Attention.mp4 | 20.8 MB | ||
| 007 Unicode Normalization - Composition and Decomposition.mp4 | 20.3 MB | ||
| 008 Fine-tuning with MLM - Training with Trainer.mp4 | 19.9 MB | ||
| 008 Implementing a BM25 Retriever.mp4 | 12.5 MB | ||
| 008 Multi-head Attention.mp4 | 13.3 MB | ||
| 008 NER With Sentiment.mp4 | 99.9 MB | ||
| 008 Unicode Normalization - NFD and NFC.mp4 | 20 MB | ||
| 009 NER With roBERTa.mp4 | 59 MB | ||
| 009 Positional Encoding.mp4 | 55.5 MB | ||
| 009 The Logic of NSP.mp4 | 20.9 MB | ||
| 009 Unicode Normalization - NFKD and NFKC.mp4 | 30.4 MB | ||
| 009 What is FAISS_.mp4 | 42.9 MB | ||
| 010 FAISS in Haystack.mp4 | 68.1 MB | ||
| 010 Fine-tuning with NSP - Data Preparation.mp4 | 78 MB | ||
| 010 Transformer Heads.mp4 | 39.8 MB | ||
| 011 Fine-tuning with NSP - DataLoader.mp4 | 14.3 MB | ||
| 011 What is DPR_.mp4 | 29.7 MB | ||
| 012 The DPR Architecture.mp4 | 14.3 MB | ||
| 012 The Logic of MLM and NSP.mp4 | 26.3 MB | ||
| 013 Fine-tuning with MLM and NSP - Data Preparation.mp4 | 43.6 MB | ||
| 013 Retriever-Reader Stack.mp4 | 75.3 MB | ||
| Downloaded from 1337x.html | 512 B | ||
| external-assets-links.txt | 1.3 KB | ||
| ▲ 113 total files | |||
Knowledge should not be limited to those who can afford it or those willing to pay for it.
If you found this course useful and are financially stable please consider supporting the creators by buying the course :)
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!
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