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Generative AI for NLP with PyTorch Capstone Project · LearnSpace
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Generative AI for NLP with PyTorch Capstone Project

Курс от IBM
Продвинутый≈ 11.4 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Get ready to put your Generative AI, Natural Language Processing (NLP), and PyTorch skills into action in this hands-on capstone project from IBM. During this course, you’ll solve a real-world text classification challenge by building an end-to-end NLP workflow, from raw text processing to model evaluation. You’ll design and implement complete pipelines, including text preprocessing, tokenization, vocabulary creation, and dataset preparation using PyTorch Dataset and DataLoader. You’ll train and compare RNN, LSTM, and Transformer models, and explore how each architecture processes language differently. Plus, you’ll fine-tune pretrained models using Hugging Face Transformers, applying techniques used in production-grade AI systems. By the end of the course, you’ll have a portfolio-worthy capstone project that showcases your ability to build, optimize, and evaluate NLP models using metrics such as accuracy and F1-score. Great for talking about in interviews. Enroll today to strengthen your Generative AI and NLP skills and showcase your expertise with this powerful, job-oriented capstone project.

Навыки, которые вы освоите

PyTorch (Machine Learning Library)Data PreprocessingNatural Language ProcessingFine-tuningRecurrent Neural Networks (RNNs)Artificial Neural NetworksModel TrainingHugging FaceDeep LearningGenerative Model ArchitecturesGenerative AILarge Language ModelingTransfer LearningModel OptimizationData ProcessingMachine Learning AlgorithmsModel EvaluationMachine Learning

Программа курса

4 модулей · 50 учебных материалов

01Text Data Preparation and Preprocessing18 материалов

Welcome to the Course

Course IntroductionВидеоCourse OverviewЧтениеReading: Project OverviewPLUGINReading: Helpful Tips for Course CompletionPLUGIN

Text Data Loading and Exploration

Учитесь у экспертов

IBM Skills Network Team

Преподаватель курса

Harish Pant

Instructor

Generative AI for NLP with PyTorch Capstone Project
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 11.4 ч

4 модулей

Язык: Английский

Часть программы вашего университета
Text Data Loading for NLPВидео
Activity: Dialogue: Clarify Your Text Data Loading ApproachDIALOGUE
Reading: Assignment Overview: Exploring and Loading a Text Corpus with PyTorchPLUGIN
Lab: Exploring and Loading a Text Corpus with PyTorchВнешний инструмент
Practice Quiz: Text Data Loading and Exploration Задание

Tokenization and Vocabulary Development

Tokenization and Vocabulary Building with PyTorchВидеоLab: Tokenization and Vocabulary Construction using PyTorchВнешний инструментPractice Quiz: Tokenization and Vocabulary DevelopmentЗадание

Data Augmentation and Batching Strategies

Data Augmentation and Batching for NLPВидеоLab: Data Augmentation and Batching Strategies using PyTorchВнешний инструментPractice Quiz: Data Augmentation and Batching StrategiesЗадание

Module Summary and Assessment

Podcast: Summary and Highlights: Text Data Preparation and PreprocessingPLUGINReading: Reference Guide: Tokenization Strategies for Sequential and Transformer ModelsPLUGINGraded Quiz: Text Data Preparation and PreprocessingЗадание
02Sequential Text Classification with RNNs and LSTMs16 материалов

Sequential Text Classification Foundations and RNN Modeling

Building Recurrent and LSTM Classifiers in PyTorchВидеоReading: RNN Limitations and the Case for LSTM ArchitecturesPLUGINReading: Sentiment Classifier Use Case OverviewPLUGINLab: Exploring Sentiment Analysis Concepts with a Real-World DatasetВнешний инструментAssignment Overview: RNN-Based Sentiment ClassifierВидеоLab: Train and Evaluate an RNN-Based Classifier in PyTorchВнешний инструментLab: Improving an RNN Sentiment Classifier with Regularization and TuningВнешний инструментPractice Quiz: Sequential Text Classification Foundations and RNN ModelingЗадание

LSTM Modeling and Sequential Model Evaluation 

Assignment Overview: LSTM-Based Text ClassifierВидеоActivity: Dialogue: Clarify LSTM Gating MechanismsDIALOGUELab: Implement and Test an LSTM-Based Classifier in PyTorchВнешний инструментA Comparative Analysis of RNN and LSTM ModelsВидеоLab: Comparative Analysis of RNN and LSTM ArchitecturesВнешний инструментPractice Quiz: LSTM Modeling and Sequential Model EvaluationЗадание

Module Summary and Assessment

Podcast: Sequential Text Classification with RNNs and LSTMsPLUGINGraded Quiz: Sequential Text Classification with RNNs and LSTMsЗадание
03Transformer Model Development and Fine-Tuning11 материалов

Transformer Architecture and Attention Mechanisms

Transformer Architecture and Attention MechanismsВидеоReading: Transformers, Attention Mechanisms, and Transfer LearningPLUGINLab: Tokenization, Positional Encoding, and Attention Mechanisms in PyTorchВнешний инструментPractice Quiz: Transformers and Attention MechanismsЗадание

Transformer Fine-Tuning for NLP

Fine-Tuning Strategies for NLP TasksВидеоLab: Fine-Tuning and Optimizing a Pretrained Transformer for Text ClassificationВнешний инструментActivity: Role-Play: Defend Your Fine-Tuning Approach to a Product LeadDIALOGUEReading: Cheat Sheet: Hugging Face Fine-Tuning Quick ReferencePLUGINPractice Quiz: Transformer Fine-Tuning for NLPЗадание

Module Summary and Assessment

Podcast: Summary and Highlights: Transformer Model Development and Fine-TuningPLUGINGraded Quiz: Transformer Model Development and Fine-TuningЗадание
04Final Project and Course Wrap-Up5 материалов

Final Project Requirements, Submission, and Evaluation

Reading: Prepare to Submit Your ProjectPLUGINFinal Project Submission and EvaluationВнешний инструментFinal: Generative AI for NLP with PyTorch CapstoneЗадание

Course Wrap-Up

Congratulations and Next StepsЧтениеTeam and AcknowledgmentsЧтение