Introduction to Transformer for NLP with Python

Introduction to Transformer for NLP with Python, available at $39.99, has an average rating of 4.75, with 34 lectures, based on 37 reviews, and has 229 subscribers.

You will learn about Chunking Bag of Words Hugging Face transformer POS tagging TF-IDF GPT-2 Token Classification BERT Stemming Lemmatization NER Preprocessing data Attention Fine-tuning This course is ideal for individuals who are Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence or Anyone passionate about Artificial Intelligence or Anyone interested in Natural Language Processing or Data Scientists who want to take their AI Skills to the next level It is particularly useful for Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence or Anyone passionate about Artificial Intelligence or Anyone interested in Natural Language Processing or Data Scientists who want to take their AI Skills to the next level.

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Summary

Title: Introduction to Transformer for NLP with Python

Price: $39.99

Average Rating: 4.75

Number of Lectures: 34

Number of Published Lectures: 34

Number of Curriculum Items: 34

Number of Published Curriculum Objects: 34

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Chunking

Bag of Words

Hugging Face transformer

POS tagging

TF-IDF

GPT-2

Token Classification

BERT

Stemming

Lemmatization

NER

Preprocessing data

Attention

Fine-tuning

Who Should Attend

Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence

Anyone passionate about Artificial Intelligence

Anyone interested in Natural Language Processing

Data Scientists who want to take their AI Skills to the next level

Target Audiences

Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence

Anyone passionate about Artificial Intelligence

Anyone interested in Natural Language Processing

Data Scientists who want to take their AI Skills to the next level

Interested in the field of Natural Language Processing  (NLP)? Then this course is for you!

Ever since Transformers arrived on the scene, deep learning hasn’t been the same.

Machine learning is able to generate text essentially indistinguishable from that created by humans

We’ve reached new state-of-the-art performance in many NLP tasks, such as machine translation, question-answering, entailment, named entity recognition, and more

In this course, you will learn very practical skills for applying transformers, and if you want, the detailed theory behind how transformers and attention work.

There are several reasons why this course is different from any other course. The first reason is that it covers all basic natural language process techniques, so you will have an understanding of what natural language processing is. The second reason is that it covers GPT-2, NER, and BERT which are very popular in natural language processing. The final reason is that you will have lots of practice projects with detailed explanations step-by-step notebook so you can read it when you have free time.

The course is split into 4 major parts:

1. Basic natural language processing

2. Fundamental Transformers

3. Text generation with GPT-2

4. Text classification

PART 1: Using Transformers

In this section, you will learn about the fundamental of the natural language process. It is really important to understand basic natural language processing before learning transformers. In this section we will cover:

1. What is natural language processing (NLP)

2. What is stemming and lemmatization

3. What is chunking

4. What is a bag of words?

In this section, we will build 3 small projects. These projects are:

1. Gender identification

2. Sentiment analyzer

3. Topic modelling

PART 2: Fundamental transformer

In this section, you will learn how transformers really work. We will also introduce the new concept called Hugging face transformer and GPT-2 to have a big understanding of how powerful the transformer is.

In this section, we will implement two projects.

IMDB project

Q&A project implementation

PART 3: Project: Text generation with GPT-2

In this project, we will generate text with GPT-2. This is a project for us to practice and reinforce what we have learned so far. It will also demonstrate how text is generated quickly with a transformer.

PART 4: Token classification.

In this section, we will learn how to classify a text using a transformer. We will also learn about NER which is also popular in transformers.  The main project in this section is about Q &A project and  it will be more advanced than the previous Q & A project.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Structure

Lecture 2: Tool will be used in this course

Lecture 3: IMPORTANT NOTES PLEASE DO NOT SKIP

Lecture 4: What is the prerequisite of this course?

Chapter 2: Basic Natural Language Processing (NLP)

Lecture 1: Introduction to Natural Language Processing

Lecture 2: Introduction to Stemming and lemmatization

Lecture 3: Introduction to chunking

Lecture 4: Bag of word Introduction

Lecture 5: Project 1: Gender Identification

Lecture 6: Project 2: Sentiment analyzer

Lecture 7: Project 3: Topic modelling

Chapter 3: Fundamental transformer

Lecture 1: Introduction to transformer (Part 1)

Lecture 2: Introduction to transformer (Part 2)

Lecture 3: Introduction to transformer (Final Part)

Lecture 4: Introduction to Hugging Face transformers Part 1

Lecture 5: Introduction to Hugging Face transformers Part 2

Lecture 6: Introduction to Hugging Face transformers Final Part

Lecture 7: IMDB project Implementation Part 1

Lecture 8: IMDB project Implementation Final Part

Lecture 9: Q & A project Implementation

Chapter 4: Project: Text generation with GPT-2

Lecture 1: Introduction and Implementation Part 1

Lecture 2: Implementation Part 2

Lecture 3: Implementation Final Part

Chapter 5: Token Classification

Lecture 1: Introduction to token classification, NER, and Pos tagging

Lecture 2: Token Classification Implementation Part 1

Lecture 3: Token Classification Implementation Part 2

Lecture 4: Token Classification Implementation Part 3

Lecture 5: Token Classification Implementation Part 4

Lecture 6: Q&A project implementation with token classification Part 1

Lecture 7: Q&A project implementation with token classification Part 2

Lecture 8: Q&A project implementation with token classification Part 3

Lecture 9: Q&A project implementation with token classification Part 4

Lecture 10: Q&A project implementation with token classification Final Part

Chapter 6: Thank you

Lecture 1: Thank you

Instructors

Hoang Quy La
Electrical Engineer

Rating Distribution

1 stars: 2 votes

2 stars: 0 votes

3 stars: 1 votes

4 stars: 4 votes

5 stars: 30 votes

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