NLP Basics:

• Introduction to Natural Language Processing (NLP) and its applications.
• Fundamentals of text preprocessing, including tokenization, stemming, and lemmatization.
• Basic techniques for text representation, such as bag-of-words and TF-IDF.
• Introduction to fundamental NLP tasks, including text classification, named entity recognition, and sentiment analysis.
• Overview of popular NLP libraries and tools like NLTK (Natural Language Toolkit) and spaCy.
• Hands-on exercises and projects to apply foundational concepts in NLP.

Sequence Models in NLP (RNN/LSTM/GRUs):

• Introduction to sequence modeling and its importance in NLP tasks.
• Overview of recurrent neural networks (RNNs) and their architecture for processing sequential data.
• Detailed explanation of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures, addressing the vanishing gradient problem.
• Exploration of applications of RNNs, LSTMs, and GRUs in various NLP tasks, including text generation, machine translation, and sentiment analysis.
• Hands-on implementation of RNNs, LSTMs, and GRUs using deep learning frameworks like TensorFlow or PyTorch.
• Evaluation and optimization techniques for improving the performance of sequence models in NLP tasks.

Advanced Networks like Transformer and BERT:

• Introduction to the Transformer architecture and its revolutionary impact on NLP.
• In-depth exploration of self-attention mechanisms and multi-head attention in Transformer models.
• Overview of pre-trained Transformer-based models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformers).
• Explanation of pre-training objectives and fine-tuning strategies for transfer learning with Transformer models.
• Discussion on recent advancements and variants of Transformer architectures, including XLNet, T5, and RoBERTa.
• Hands-on labs and projects to implement and fine-tune Transformer-based models for various NLP tasks, such as text classification, question answering, and language generation.

NLP using Hugging Face:

• Introduction to the Hugging Face library and its role in simplifying NLP workflows.
• Overview of Hugging Face’s Transformers library, providing access to pretrained models for NLP tasks.
• Exploration of Hugging Face pipelines for performing common NLP tasks like text generation, sentiment analysis, and named entity recognition with minimal code.
• Introduction to model fine-tuning and transfer learning techniques using Hugging Face’s Trainer API.
• Hands-on tutorials and projects demonstrating how to leverage Hugging Face for building and deploying NLP applications efficiently.
• Advanced topics like model distillation, model compression, and deployment strategies using Hugging Face Transformers.