Natural Language Generation: Evaluating Text Quality

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Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Prompting and Alignment

Explores prompting, alignment, and the capabilities of large language models for natural language processing tasks.

Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

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.

Word Embeddings: Introduction and Applications

Introduces word embeddings, explaining how they capture word meanings based on context and their applications in natural language processing tasks.

Natural Language Processing

Introduces Natural Language Processing, covering text preprocessing, sentiment analysis, and topic analysis, with a focus on building a climate change risk index.

Ethics in Natural Language Processing: Addressing Bias and Misinformation

Covers ethical considerations in NLP, focusing on bias, toxicity, and misinformation.

Modern NLP and Ethics in NLP

Delves into advancements and challenges in NLP, along with ethical considerations and potential harms.

Ethical Considerations in Natural Language Processing

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

BERT: Pretraining and Applications

Delves into BERT pretraining for transformers, discussing its applications in NLP tasks.