Natural Language Processing (nlp) With Transformers: Ai For Understanding Language

Introduction:

Transformers have revolutionized Natural Language Processing (NLP), enabling AI to understand and generate human language with unprecedented accuracy. This course on NLP with Transformers equips participants with the specialized knowledge and skills to build and deploy AI models for advanced text and language processing tasks. Participants will learn how to utilize transformer architectures, fine-tune pre-trained models, and apply NLP for various real-world applications. This course bridges the gap between traditional NLP methods and cutting-edge transformer-based techniques, empowering professionals to harness the power of AI for language understanding.

Target Audience:

This course is designed for professionals seeking to apply transformer-based NLP techniques, including:

• AI/ML Engineers
• Data Scientists
• NLP Developers
• Software Engineers
• Content Analysts
• Anyone interested in building AI-powered language applications

Course Objectives:

Upon completion of this NLP with Transformers course, participants will be able to:

• Understand the architecture and principles of transformer models.
• Fine-tune pre-trained transformer models for specific NLP tasks.
• Implement NLP tasks such as sentiment analysis, text summarization, and machine translation.
• Utilize transformer models for question answering and information retrieval.
• Understand the challenges and limitations of transformer-based NLP.
• Apply NLP for text classification, named entity recognition, and language generation.
• Develop strategies for deploying NLP models in real-world applications.
• Understand the ethical considerations of using NLP.
• Enhance their ability to build AI-powered language applications.
• Improve their skills in analyzing and processing text and language data.
• Contribute to improved language understanding and automation within their organization.
• Stay up-to-date with the latest trends and best practices in NLP with transformers.
• Become a more knowledgeable and effective NLP professional.
• Understand ethical considerations in NLP applications.
• Learn how to use transformer-based NLP frameworks and tools effectively.

DURATION

10 Days

COURSE CONTENT

Module 1: Introduction to Natural Language Processing (NLP) and Transformers

• Understanding the evolution of NLP and its applications.
• Limitations of traditional NLP methods and the rise of transformers.
• Overview of transformer architectures and their impact on NLP.
• Key concepts: attention mechanisms, positional encoding, and self-attention.
• Setting up the development environment (Hugging Face Transformers, PyTorch, TensorFlow).

Module 2: Transformer Architecture and Attention Mechanisms

• Deep dive into the encoder-decoder architecture of transformers.
• Understanding multi-head attention and its significance.
• Exploring different types of attention mechanisms (self-attention, cross-attention).
• Understanding positional encoding and its role in sequential data.
• Implementing and visualizing attention mechanisms.

Module 3: Pre-trained Transformer Models (BERT, GPT, RoBERTa, etc.)

• Understanding the training objectives and methodologies of pre-trained models.
• Exploring different pre-trained model architectures and their strengths.
• Utilizing pre-trained models for feature extraction and transfer learning.
• Understanding the concept of masked language modeling and next sentence prediction.
• Choosing the right pre-trained model for specific NLP tasks.

Module 4: Tokenization and Text Representation

• Understanding different tokenization techniques (word-level, subword-level, character-level).
• Exploring Byte-Pair Encoding (BPE) and WordPiece tokenization.
• Utilizing tokenizers from Hugging Face Transformers.
• Understanding embeddings and their role in text representation.
• Converting text data into numerical representations for transformer models.

Module 5: Fine-tuning Transformer Models for Text Classification

• Understanding the fine-tuning process and its importance.
• Preparing datasets for text classification tasks.
• Implementing fine-tuning using Hugging Face Transformers.
• Evaluating and visualizing model performance.
• Addressing overfitting and underfitting in fine-tuning.

Module 6: Fine-tuning Transformer Models for Named Entity Recognition (NER)

• Understanding NER and its applications.
• Preparing datasets for NER tasks.
• Implementing fine-tuning for NER using transformer models.
• Utilizing sequence tagging techniques for NER.
• Evaluating NER model performance.

Module 7: Fine-tuning Transformer Models for Question Answering

• Understanding question answering tasks and datasets (e.g., SQuAD).
• Implementing fine-tuning for extractive question answering.
• Utilizing span prediction techniques for question answering.
• Evaluating question answering model performance.
• Implementing abstractive question answering.

Module 8: Text Summarization with Transformers

• Understanding different approaches to text summarization (extractive, abstractive).
• Implementing fine-tuning for text summarization tasks.
• Utilizing transformer models for abstractive summarization.
• Evaluating summarization model performance (ROUGE scores).
• Generating summaries of different lengths and styles.

Module 9: Machine Translation with Transformers

• Understanding machine translation tasks and datasets.
• Implementing fine-tuning for machine translation using transformer models.
• Utilizing encoder-decoder architectures for translation tasks.
• Evaluating translation model performance (BLEU scores).
• Addressing challenges in low-resource language translation.

Module 10: Language Generation with GPT Models

• Understanding the capabilities and limitations of GPT models.
• Utilizing GPT models for text generation and completion.
• Implementing prompt engineering techniques for controlled generation.
• Exploring different generation strategies (e.g., greedy decoding, beam search).
• Evaluating the quality and coherence of generated text.

Module 11: Sentiment Analysis and Emotion Detection

• Understanding sentiment analysis and emotion detection tasks.
• Implementing fine-tuning for sentiment analysis and emotion detection.
• Utilizing transformer models for aspect-based sentiment analysis.
• Evaluating sentiment analysis model performance.
• Addressing challenges in handling sarcasm and irony.

Module 12: Information Retrieval and Semantic Search

• Understanding information retrieval and semantic search tasks.
• Utilizing transformer models for semantic similarity and relevance ranking.
• Implementing dense retrieval techniques.
• Building semantic search applications.
• Evaluating information retrieval performance.

Module 13: Advanced NLP Techniques and Applications

• Exploring advanced NLP tasks (e.g., topic modeling, relation extraction).
• Utilizing transformer models for multimodal NLP (text and images).
• Implementing cross-lingual NLP techniques.
• Exploring the use of transformers for code generation and analysis.
• Understanding the applications of transformers in dialogue systems.

Module 14: Ethical Considerations and Bias in NLP

• Understanding the ethical implications of NLP applications.
• Addressing bias and fairness in NLP models.
• Ensuring data privacy and security in NLP.
• Understanding the impact of NLP on society.
• Developing responsible NLP practices.

Module 15: Deploying NLP Models and Building Applications

• Deploying NLP models in cloud and edge environments.
• Utilizing containerization and orchestration for model deployment.
• Building end-to-end NLP applications.
• Monitoring and maintaining deployed models.
• Continuous learning and professional development in NLP with transformers.

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 5 working days before commencement of the training.