Natural Language Processing- NLP With Transformers in Python

Natural Language Processing: NLP With Transformers in Python, available at $84.99, has an average rating of 4.47, with 105 lectures, based on 2110 reviews, and has 28414 subscribers.

You will learn about 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 This course is ideal for individuals who are Aspiring data scientists and ML engineers interested in NLP or Practitioners looking to upgrade their skills or Developers looking to implement NLP solutions or Data scientist or Machine Learning Engineer or Python Developers It is particularly useful for Aspiring data scientists and ML engineers interested in NLP or Practitioners looking to upgrade their skills or Developers looking to implement NLP solutions or Data scientist or Machine Learning Engineer or Python Developers.

Enroll now: Natural Language Processing: NLP With Transformers in Python

Summary

Title: Natural Language Processing: NLP With Transformers in Python

Price: $84.99

Average Rating: 4.47

Number of Lectures: 105

Number of Published Lectures: 104

Number of Curriculum Items: 109

Number of Published Curriculum Objects: 108

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Aspiring data scientists and ML engineers interested in NLP

Practitioners looking to upgrade their skills

Developers looking to implement NLP solutions

Data scientist

Machine Learning Engineer

Python Developers

Target Audiences

Aspiring data scientists and ML engineers interested in NLP

Practitioners looking to upgrade their skills

Developers looking to implement NLP solutions

Data scientist

Machine Learning Engineer

Python Developers

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 cover everything you need 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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course Overview

Lecture 3: Hello! and Further Resources

Lecture 4: Environment Setup

Lecture 5: Alternative Local Setup

Lecture 6: Alternative Colab Setup

Lecture 7: CUDA Setup

Lecture 8: Apple Silicon Setup

Chapter 2: NLP and Transformers

Lecture 1: The Three Eras of AI

Lecture 2: Pros and Cons of Neural AI

Lecture 3: Word Vectors

Lecture 4: Recurrent Neural Networks

Lecture 5: Long Short-Term Memory

Lecture 6: Encoder-Decoder Attention

Lecture 7: Self-Attention

Lecture 8: Multi-head Attention

Lecture 9: Positional Encoding

Lecture 10: Transformer Heads

Chapter 3: Preprocessing for NLP

Lecture 1: Stopwords

Lecture 2: Tokens Introduction

Lecture 3: Model-Specific Special Tokens

Lecture 4: Stemming

Lecture 5: Lemmatization

Lecture 6: Unicode Normalization – Canonical and Compatibility Equivalence

Lecture 7: Unicode Normalization – Composition and Decomposition

Lecture 8: Unicode Normalization – NFD and NFC

Lecture 9: Unicode Normalization – NFKD and NFKC

Chapter 4: Attention

Lecture 1: Attention Introduction

Lecture 2: Alignment With Dot-Product

Lecture 3: Dot-Product Attention

Lecture 4: Self Attention

Lecture 5: Bidirectional Attention

Lecture 6: Multi-head and Scaled Dot-Product Attention

Chapter 5: Language Classification

Lecture 1: Introduction to Sentiment Analysis

Lecture 2: Prebuilt Flair Models

Lecture 3: Introduction to Sentiment Models With Transformers

Lecture 4: Tokenization And Special Tokens For BERT

Lecture 5: Making Predictions

Chapter 6: [Project] Sentiment Model With TensorFlow and Transformers

Lecture 1: Project Overview

Lecture 2: Getting the Data (Kaggle API)

Lecture 3: Preprocessing

Lecture 4: Building a Dataset

Lecture 5: Dataset Shuffle, Batch, Split, and Save

Lecture 6: Build and Save

Lecture 7: Loading and Prediction

Chapter 7: Long Text Classification With BERT

Lecture 1: Classification of Long Text Using Windows

Lecture 2: Window Method in PyTorch

Chapter 8: Named Entity Recognition (NER)

Lecture 1: Introduction to spaCy

Lecture 2: Extracting Entities

Lecture 3: Authenticating With The Reddit API

Lecture 4: Pulling Data With The Reddit API

Lecture 5: Extracting ORGs From Reddit Data

Lecture 6: Getting Entity Frequency

Lecture 7: Entity Blacklist

Lecture 8: NER With Sentiment

Lecture 9: NER With roBERTa

Chapter 9: Question and Answering

Lecture 1: Open Domain and Reading Comprehension

Lecture 2: Retrievers, Readers, and Generators

Lecture 3: Intro to SQuAD 2.0

Lecture 4: Processing SQuAD Training Data

Lecture 5: (Optional) Processing SQuAD Training Data with Match-Case

Lecture 6: Our First Q&A Model

Chapter 10: Metrics For Language

Lecture 1: Q&A Performance With Exact Match (EM)

Lecture 2: Introducing the ROUGE Metric

Lecture 3: ROUGE in Python

Lecture 4: Applying ROUGE to Q&A

Lecture 5: Recall, Precision and F1

Lecture 6: Longest Common Subsequence (LCS)

Chapter 11: Reader-Retriever QA With Haystack

Lecture 1: Intro to Retriever-Reader and Haystack

Lecture 2: What is Elasticsearch?

Lecture 3: Elasticsearch Setup (Windows)

Lecture 4: Elasticsearch Setup (Linux)

Lecture 5: Elasticsearch in Haystack

Lecture 6: Sparse Retrievers

Lecture 7: Cleaning the Index

Lecture 8: Implementing a BM25 Retriever

Lecture 9: What is FAISS?

Lecture 10: Further Materials for Faiss

Lecture 11: FAISS in Haystack

Lecture 12: What is DPR?

Lecture 13: The DPR Architecture

Lecture 14: Retriever-Reader Stack

Chapter 12: [Project] Open-Domain QA

Lecture 1: ODQA Stack Structure

Lecture 2: Creating the Database

Lecture 3: Building the Haystack Pipeline

Chapter 13: Similarity

Instructors

James Briggs
ML Engineer

Rating Distribution

1 stars: 39 votes

2 stars: 57 votes

3 stars: 182 votes

4 stars: 695 votes

5 stars: 1137 votes

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