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    Natural Language Processing With Transformers In Python

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    Natural Language Processing With Transformers In Python - Online Course Download
    Natural Language Processing With Transformers In Python - Online Course Download
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    Data Science Development free online course free udemy paid course freecourse freecoursesite Natural Language Processing udemy course download udemy free download

    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

    Requirements :

    Knowledge of Python
    Experience in data science a plus
    Experience in NLP a plus

    Description :

    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!

    Who this course is for :

    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

    Course Size Details :

    11.5 hours on-demand video
    1 article
    Full lifetime access
    Access on mobile and TV
    Assignments
    Certificate of completion

    View Demo

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