Jul

24

2022

Introduction to Transformer for NLP with Python

supnatural 24 Jul 2022 12:14 LEARNING » e-learning - Tutorial

Introduction to Transformer for NLP with Python
Introduction to Transformer for NLP with Python
Published 07/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 33 lectures (7h 1m) | Size: 2.92 GB


BERT, GPT, Deep Learning, Machine Learning, & NLP with Hugging Face, Attention in Python, Tensorflow, PyTorch, & Keras

What you'll 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
Requirements
Expert in Pytorch
Expert in Recurrent Neural Network
Expert in Python programming language
Description
Interested in the field of Natural Language Processing (NLP)? Then this course is for you!
This course has been designed by a software engineer. I hope with the experience and knowledge I did gain throughout the years, I can share my knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.
I will walk you step-by-step into the transformer which is a very powerful tool in Natural Language Processing. With every tutorial, you will develop new skills and improve your understanding of transformers in Natural Language Processing.
This course is fun and exciting, but at the same time, we dive deep into transformer. Throughout the brand new version of the course, we cover tons of tools and technologies including
Deep Learning.
Google Colab
Keras.
Matplotlib.
Splitting Data into Training Set and Test Set.
Training Neural Network.
Model building.
Analyzing Results.
Model compilation.
Make a Prediction.
Testing Accuracy.
Confusion Matrix.
Numpy.
Pandas.
Tensorflow.
Chunking
Bag of Words
Hugging Face transformer
POS tagging
TF-IDF
GPT-2
Token Classification
BERT
Stemming
Lemmatization
NER
Preprocessing data.
Attention
Fine-tuning
Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are several projects for you to practice and build up your knowledge. These projects are listed below
Gender Identification.
Sentiment Analyzer.
Topic Modelling
IMDB Project.
QA project.
Text generation project.
Who this course is for
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
Homepage

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