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    • Natural Language Processing NLP with Transformers in Python

    Natural Language Processing: NLP With Transformers in Python

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    Udemy

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    Mode

    Online

    Fees

    ₹ 499 3299

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 499  ₹3,299
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction

    • Introduction
    • Course Overview
    • Environment Setup
    • Alternative Local Setup
    • Alternative Colab Setup
    • CUDA Setup

    NLP and Transformers

    • The Three Eras of AI
    • Pros and Cons of Neural AI
    • Word Vectors
    • Recurrent Neural Networks
    • Long Short-Term Memory
    • Encoder-Decoder Attention
    • Self-Attention
    • Multi-head Attention
    • Positional Encoding
    • Transformer Heads

    Preprocessing for NLP

    • Stopwords
    • Tokens Introduction
    • Model-Specific Special Tokens
    • Stemming
    • Lemmatization
    • Unicode Normalization - Canonical and Compatibility Equivalence
    • Unicode Normalization - Composition and Decomposition
    • Unicode Normalization - NFD and NFC
    • Unicode Normalization - NFKD and NFKC

    Attention

    • Attention Introduction
    • Alignment With Dot-Product
    • Dot-Product Attention
    • Self Attention
    • Bidirectional Attention
    • Multi-head and Scaled Dot-Product Attention

    Language Classification

    • Introduction to Sentiment Analysis
    • Prebuilt Flair Models
    • Introduction to Sentiment Models With Transformers
    • Tokenization And Special Tokens For BERT
    • Making Predictions

    [Project] Sentiment Model With TensorFlow and Transformers

    • Project Overview
    • Getting the Data (Kaggle API)
    • Preprocessing
    • Building a Dataset
    • Dataset Shuffle, Batch, Split, and Save
    • Build and Save
    • Loading and Prediction

    Long Text Classification With BERT

    • Classification of Long Text Using Windows
    • Window Method in PyTorch

    Named Entity Recognition (NER)

    • Introduction to spaCy
    • Extracting Entities
    • NER Walkthrough
    • Authenticating With The Reddit API
    • Pulling Data With The Reddit API
    • Extracting ORGs From Reddit Data
    • Getting Entity Frequency
    • Entity Blacklist
    • NER With Sentiment
    • NER With roBERTa

    Question and Answering

    • Open Domain and Reading Comprehension
    • Retrievers, Readers, and Generators
    • Intro to SQuAD 2.0
    • Processing SQuAD Training Data
    • (Optional) Processing SQuAD Training Data with Match-Case
    • Processing SQuAD Dev Data
    • Our First Q&A Model

    Metrics For Language

    • Q&A Performance With Exact Match (EM)
    • Introducing the ROUGE Metric
    • ROUGE in Python
    • Applying ROUGE to Q&A
    • Recall, Precision and F1
    • Longest Common Subsequence (LCS)

    Reader-Retriever QA With Haystack

    • Intro to Retriever-Reader and Haystack
    • What is Elasticsearch?
    • Elasticsearch Setup (Windows)
    • Elasticsearch Setup (Linux)
    • Elasticsearch in Haystack
    • Sparse Retrievers
    • Cleaning the Index
    • Implementing a BM25 Retriever
    • What is FAISS?
    • Further Materials for Faiss
    • FAISS in Haystack
    • What is DPR?
    • The DPR Architecture
    • Retriever-Reader Stack

    [Project] Open-Domain QA

    • ODQA Stack Structure
    • Creating the Database
    • Building the Haystack Pipeline

    Similarity

    • Introduction to Similarity
    • Extracting The Last Hidden State Tensor
    • Sentence Vectors With Mean Pooling
    • Using Cosine Similarity
    • Similarity With Sentence-Transformers
    • Further Learning

    Pre-Training Transformer Models

    • Visual Guide to BERT Pretraining
    • Introduction to BERT For Pretraining Code
    • BERT Pretraining - Masked-Language Modeling (MLM)
    • BERT Pretraining - Next Sentence Prediction (NSP)
    • The Logic of MLM
    • Pre-training with MLM - Data Preparation
    • Pre-training with MLM - Training
    • Pre-training with MLM - Training with Trainer
    • The Logic of NSP
    • Pre-training with NSP - Data Preparation
    • Pre-training with NSP - DataLoader
    • Setup the NSP Pre-training Training Loop
    • The Logic of MLM and NSP
    • Pre-training with MLM and NSP - Data Preparation
    • Setup DataLoader and Model Pre-training For MLM and NSP

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