Natural Language Processing (nlp) With Transformers Training Course: Bert & Gpt Mastery

Introduction

Unlock the power of modern language understanding with our Natural Language Processing (NLP) with Transformers Training Course. This program is designed to provide you with in-depth knowledge and practical skills in utilizing transformer models (BERT, GPT) for advanced text analysis and generation, enabling you to build cutting-edge NLP applications. In today's AI-driven world, mastering transformer models is crucial for developing sophisticated solutions in various fields, from sentiment analysis to text summarization. Our NLP with transformers training course offers hands-on experience and expert guidance, empowering you to implement state-of-the-art NLP techniques.

This NLP with BERT & GPT training delves into the core concepts of transformer architectures, covering topics such as BERT for understanding text, GPT for generating text, and fine-tuning techniques for specific NLP tasks. You'll gain expertise in using industry-standard libraries and tools to utilize transformer models (BERT, GPT) for advanced text analysis and generation, meeting the demands of modern NLP projects. Whether you're a data scientist, NLP engineer, or researcher, this Natural Language Processing (NLP) with Transformers course will empower you to build powerful language models.

Target Audience:

• Data Scientists
• NLP Engineers
• AI Developers
• Machine Learning Engineers
• Researchers
• Software Developers
• Anyone needing advanced NLP skills

Course Objectives:

• Understand the fundamentals of NLP with transformer models.
• Master the architecture and implementation of BERT.
• Utilize GPT for text generation and language modeling.
• Implement fine-tuning techniques for specific NLP tasks.
• Design and build NLP models for various applications.
• Optimize NLP models for performance and accuracy.
• Troubleshoot and address complex NLP challenges.
• Implement model evaluation and validation techniques for NLP.
• Integrate transformer models into real-world applications.
• Understand how to tune hyperparameters for optimal performance.
• Explore advanced NLP techniques with transformers.
• Apply real world use cases for BERT and GPT.
• Leverage transformer libraries for efficient NLP implementation.

Duration

10 Days

Course content

Module 1: Introduction to NLP with Transformers

• Fundamentals of NLP with transformer models.
• Overview of BERT and GPT architectures.
• Setting up an NLP development environment.
• Introduction to transformer libraries and tools.
• Best practices for NLP with transformers.

Module 2: BERT for Text Understanding

• Implementing BERT for text classification.
• Utilizing BERT for named entity recognition (NER).
• Designing and building question-answering systems with BERT.
• Optimizing BERT for text understanding tasks.
• Best practices for BERT.

Module 3: GPT for Text Generation

• Implementing GPT for text generation and completion.
• Utilizing GPT for language modeling tasks.
• Designing and building conversational AI models with GPT.
• Optimizing GPT for creative text generation.
• Best practices for GPT.

Module 4: Fine-Tuning Transformer Models

• Implementing fine-tuning techniques for specific NLP tasks.
• Utilizing transfer learning for efficient model training.
• Designing and building task-specific NLP models.
• Optimizing fine-tuning strategies for performance.
• Best practices for fine-tuning.

Module 5: NLP Model Design

• Designing NLP models for specific applications.
• Implementing model architectures for various NLP tasks.
• Utilizing transformer models for text summarization.
• Optimizing model design for performance.
• Best practices for NLP model design.

Module 6: Model Optimization and Performance

• Optimizing NLP models for accuracy and efficiency.
• Utilizing hyperparameter tuning techniques.
• Implementing model compression and acceleration.
• Designing scalable NLP solutions.
• Best practices for model optimization.

Module 7: Troubleshooting NLP Challenges

• Debugging complex NLP issues.
• Analyzing model performance and errors.
• Utilizing troubleshooting techniques for model improvement.
• Resolving common NLP challenges.
• Best practices for troubleshooting.

Module 8: Model Evaluation and Validation

• Implementing evaluation metrics for NLP tasks.
• Utilizing cross-validation techniques for NLP models.
• Designing and building model validation pipelines.
• Optimizing model evaluation strategies.
• Best practices for model evaluation.

Module 9: Integration with Real-World Applications

• Integrating transformer models into production systems.
• Utilizing APIs and deployment tools for NLP.
• Implementing real-time NLP applications.
• Optimizing models for deployment environments.
• Best practices for integration.

Module 10: Hyperparameter Tuning

• Utilizing grid search and random search for tuning.
• Implementing Bayesian optimization for hyperparameter selection.
• Designing and building hyperparameter tuning pipelines.
• Optimizing hyperparameters for model performance.
• Best practices for hyperparameter tuning.

Module 11: Advanced NLP Techniques

• Implementing advanced NLP techniques with transformers.
• Utilizing transformer models for sentiment analysis.
• Implementing transformer models for semantic search.
• Advanced techniques for NLP with transformers.
• Best practices for advanced techniques.

Module 12: Real-World Use Cases

• Implementing BERT for customer support chatbots.
• Utilizing GPT for content generation and marketing.
• Implementing transformer models for legal document analysis.
• Utilizing transformer models for social media monitoring.
• Best practices for real-world applications.

Module 13: Transformer Libraries Implementation

• Utilizing Hugging Face Transformers for NLP tasks.
• Implementing transformer models with TensorFlow and PyTorch.
• Designing and building NLP pipelines with transformer libraries.
• Optimizing library usage for efficient implementation.
• Best practices for library implementation.

Module 14: Model Interpretability

• Implementing model interpretability techniques for NLP.
• Utilizing visualization tools for model understanding.
• Designing and building interpretable NLP models.
• Optimizing model transparency.
• Best practices for model interpretability.

Module 15: Future Trends in NLP with Transformers

• Emerging trends in NLP with transformers.
• Utilizing large language models for advanced applications.
• Implementing multimodal transformers for cross-domain tasks.
• Best practices for future NLP.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.