This course provides a comprehensive introduction to deep learning techniques for natural language processing (NLP). It begins with fundamental methods for text processing using recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and gated recurrent units (GRUs). The course then explores advanced transformer-based architectures, such as BERT and GPT, focusing on their attention mechanisms and their impact on modern NLP applications. Participants will learn how to apply these models to real-world tasks, including sentiment analysis, machine translation, and text summarization, through practical examples and hands-on exercises.

• Introduction to NLP and Deep Learning: Overview of natural language processing and the role of deep learning. 
• Text Processing with RNN, LSTM, and GRU:  Understanding sequential data, recurrent neural networks, and their variations (LSTM, GRU) for text modeling. 
• Transformers and Attention Mechanisms:  Introduction to self-attention and transformer architectures, including BERT and GPT. 
• Sentiment Analysis: Using deep learning models to analyze sentiment in text data. 
• Machine Translation Applying sequence-to-sequence models and transformers for language translation. 
• Text Summarization Exploring extractive and abstractive summarization techniques with deep learning. 
• Hands-on Implementation and Case Studies

On successful completion of this course, learners will be able to: 

• Understand the basics of NLP and deep learning techniques. 
• Use RNNs, LSTMs, and GRUs for text processing. 
• Explore transformer-based models like BERT and GPT. 
• Perform simple sentiment analysis tasks. 
• Understand the principles of machine translation.
•  Learn basic text summarization techniques.

The course follows a traditional classroom format with slide presentations, fostering interactive engagement and actve partcipaton. Partcipants will also apply machine learning algorithms using Python, with guidance on implementng them in practcal scenarios.