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    Introduction to Transformer for NLP with Python

    Posted By: BlackDove
    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