Modern NLP: Introduction

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Explores the evolution and challenges of Neural Machine Translation systems and the evaluation metrics used in this field.

Neuro-symbolic Representations: Commonsense Knowledge & Reasoning

Explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, emphasizing the challenges and limitations of deep learning in natural language processing.

Natural Language Generation: Understanding the Task and Techniques

Provides an overview of Natural Language Generation, focusing on tasks, challenges, and methodologies for creating coherent text.

Sequence to Sequence Models: Overview and Attention Mechanisms

Explores sequence to sequence models, attention mechanisms, and their role in addressing model limitations and improving interpretability.

Introduction to Natural Language Processing

Introduces the basics of Natural Language Processing, covering challenges, application domains, and linguistic processing levels.

Machine Translation: Sequence-to-Sequence and Attention

Explores the advancements in Machine Translation, focusing on Sequence-to-Sequence models and Attention mechanisms.

Non conceptual knowledge systems

Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.

Natural Language Generation

Explores Natural Language Generation, covering neural models, biases, ethics, and evaluation challenges.

Deep Learning Techniques: Recurring Networks and LSTM Models

Discusses the implementation and optimization of recurring networks using LSTM models in deep learning.

Ethical Considerations in Natural Language Processing

Explores ethical challenges in NLP systems, including biases, toxicity, privacy, and disinformation.