Offered by deeplearning.ai on Coursera, this specialization covers essential topics in natural language processing (NLP). It includes courses on sequence models, attention models, and transformer networks, which are foundational for understanding and building ChatGPT-like models.
"Transformers for Natural Language Processing"
This course, offered by Hugging Face, provides a comprehensive understanding of transformer-based models and their applications in NLP. It covers the theory behind transformers, as well as hands-on exercises using Hugging Face's Transformers library, which is commonly used for building and fine-tuning models like ChatGPT.
"Advanced NLP with Spacy"
Spacy is a popular Python library for NLP tasks. This course, offered by Explosion, the creators of Spacy, provides in-depth knowledge of advanced NLP techniques using Spacy. It covers topics such as text classification, named entity recognition, and dependency parsing, which are essential for understanding the intricacies of prompt engineering.
"Deep Learning Specialization"
Offered by deeplearning.ai on Coursera, this specialization covers various deep learning topics, including neural networks, convolutional neural networks, recurrent neural networks, and generative models. These concepts are fundamental to understanding the inner workings of ChatGPT and prompt engineering techniques.
"Machine Learning for Sequential Data"
Offered by Stanford University on Coursera, this course focuses on techniques for analyzing sequential data, such as text and speech. It covers topics like recurrent neural networks, attention mechanisms, and reinforcement learning for sequence prediction tasks. This course will provide you with the necessary skills to work with sequential data in the context of ChatGPT and prompt engineering.
These courses provide a solid foundation in NLP, deep learning, and sequential data analysis, which are crucial for mastering ChatGPT and prompt engineering techniques. It's recommended to supplement these courses with hands-on projects and continuous learning from research papers, online resources, and open-source code repositories to further enhance your understanding and expertise.