15 Topics
10 Days
Programme Overview
Training Description
Who Should Attend
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
Session Objectives
- 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.
About the Course
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..
Curriculum & Topics
15 Topics | 10 Days
-
Subtopic 1.1:
Understanding the evolution of NLP and its applications.
-
Subtopic 1.2:
Limitations of traditional NLP methods and the rise of transformers.
-
Subtopic 1.3:
Overview of transformer architectures and their impact on NLP.
-
Subtopic 1.4:
Key concepts: attention mechanisms, positional encoding, and self-attention.
-
Subtopic 1.5:
Setting up the development environment (Hugging Face Transformers, PyTorch, TensorFlow).
-
Subtopic 2.1:
Deep dive into the encoder-decoder architecture of transformers.
-
Subtopic 2.2:
Understanding multi-head attention and its significance.
-
Subtopic 2.3:
Exploring different types of attention mechanisms (self-attention, cross-attention).
-
Subtopic 2.4:
Understanding positional encoding and its role in sequential data.
-
Subtopic 2.5:
Implementing and visualizing attention mechanisms.
-
Subtopic 3.1:
Understanding the training objectives and methodologies of pre-trained models.
-
Subtopic 3.2:
Exploring different pre-trained model architectures and their strengths.
-
Subtopic 3.3:
Utilizing pre-trained models for feature extraction and transfer learning.
-
Subtopic 3.4:
Understanding the concept of masked language modeling and next sentence prediction.
-
Subtopic 3.5:
Choosing the right pre-trained model for specific NLP tasks.
-
Subtopic 4.1:
Understanding different tokenization techniques (word-level, subword-level, character-level).
-
Subtopic 4.2:
Exploring Byte-Pair Encoding (BPE) and WordPiece tokenization.
-
Subtopic 4.3:
Utilizing tokenizers from Hugging Face Transformers.
-
Subtopic 4.4:
Understanding embeddings and their role in text representation.
-
Subtopic 4.5:
Converting text data into numerical representations for transformer models.
-
Subtopic 5.1:
Understanding the fine-tuning process and its importance.
-
Subtopic 5.2:
Preparing datasets for text classification tasks.
-
Subtopic 5.3:
Implementing fine-tuning using Hugging Face Transformers.
-
Subtopic 5.4:
Evaluating and visualizing model performance.
-
Subtopic 5.5:
Addressing overfitting and underfitting in fine-tuning.
-
Subtopic 6.1:
Understanding NER and its applications.
-
Subtopic 6.2:
Preparing datasets for NER tasks.
-
Subtopic 6.3:
Implementing fine-tuning for NER using transformer models.
-
Subtopic 6.4:
Utilizing sequence tagging techniques for NER.
-
Subtopic 6.5:
Evaluating NER model performance.
-
Subtopic 7.1:
Understanding question answering tasks and datasets (e.g., SQuAD).
-
Subtopic 7.2:
Implementing fine-tuning for extractive question answering.
-
Subtopic 7.3:
Utilizing span prediction techniques for question answering.
-
Subtopic 7.4:
Evaluating question answering model performance.
-
Subtopic 7.5:
Implementing abstractive question answering.
-
Subtopic 8.1:
Understanding different approaches to text summarization (extractive, abstractive).
-
Subtopic 8.2:
Implementing fine-tuning for text summarization tasks.
-
Subtopic 8.3:
Utilizing transformer models for abstractive summarization.
-
Subtopic 8.4:
Evaluating summarization model performance (ROUGE scores).
-
Subtopic 8.5:
Generating summaries of different lengths and styles.
-
Subtopic 9.1:
Understanding machine translation tasks and datasets.
-
Subtopic 9.2:
Implementing fine-tuning for machine translation using transformer models.
-
Subtopic 9.3:
Utilizing encoder-decoder architectures for translation tasks.
-
Subtopic 9.4:
Evaluating translation model performance (BLEU scores).
-
Subtopic 9.5:
Addressing challenges in low-resource language translation.
-
Subtopic 10.1:
Understanding the capabilities and limitations of GPT models.
-
Subtopic 10.2:
Utilizing GPT models for text generation and completion.
-
Subtopic 10.3:
Implementing prompt engineering techniques for controlled generation.
-
Subtopic 10.4:
Exploring different generation strategies (e.g., greedy decoding, beam search).
-
Subtopic 10.5:
Evaluating the quality and coherence of generated text.
-
Subtopic 11.1:
Understanding sentiment analysis and emotion detection tasks.
-
Subtopic 11.2:
Implementing fine-tuning for sentiment analysis and emotion detection.
-
Subtopic 11.3:
Utilizing transformer models for aspect-based sentiment analysis.
-
Subtopic 11.4:
Evaluating sentiment analysis model performance.
-
Subtopic 11.5:
Addressing challenges in handling sarcasm and irony.
-
Subtopic 12.1:
Understanding information retrieval and semantic search tasks.
-
Subtopic 12.2:
Utilizing transformer models for semantic similarity and relevance ranking.
-
Subtopic 12.3:
Implementing dense retrieval techniques.
-
Subtopic 12.4:
Building semantic search applications.
-
Subtopic 12.5:
Evaluating information retrieval performance.
-
Subtopic 13.1:
Exploring advanced NLP tasks (e.g., topic modeling, relation extraction).
-
Subtopic 13.2:
Utilizing transformer models for multimodal NLP (text and images).
-
Subtopic 13.3:
Implementing cross-lingual NLP techniques.
-
Subtopic 13.4:
Exploring the use of transformers for code generation and analysis.
-
Subtopic 13.5:
Understanding the applications of transformers in dialogue systems.
-
Subtopic 14.1:
Understanding the ethical implications of NLP applications.
-
Subtopic 14.2:
Addressing bias and fairness in NLP models.
-
Subtopic 14.3:
Ensuring data privacy and security in NLP.
-
Subtopic 14.4:
Understanding the impact of NLP on society.
-
Subtopic 14.5:
Developing responsible NLP practices.
-
Subtopic 15.1:
Deploying NLP models in cloud and edge environments.
-
Subtopic 15.2:
Utilizing containerization and orchestration for model deployment.
-
Subtopic 15.3:
Building end-to-end NLP applications.
-
Subtopic 15.4:
Monitoring and maintaining deployed models.
-
Subtopic 15.5:
Continuous learning and professional development in NLP with transformers.
