{"id":11273,"date":"2026-08-10T10:30:03","date_gmt":"2026-08-10T10:30:03","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/linkedin-deep-learning-with-python-sequence-models-and-transformers-2025-4\/"},"modified":"2026-08-10T10:30:03","modified_gmt":"2026-08-10T10:30:03","slug":"linkedin-deep-learning-with-python-sequence-models-and-transformers-2025-4","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/linkedin-deep-learning-with-python-sequence-models-and-transformers-2025-4\/","title":{"rendered":"LinkedIn \u2013 Deep Learning with Python: Sequence Models and Transformers 2025-4"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Deep Learning with Python: Sequence Models and Transformers. This course teaches sequence models and transformers in deep learning with Python and explores their applications in sequential data analysis such as time series and natural language processing (NLP). With a focus on practical aspects, the course includes coding exercises and instructor Fred Nwanganga teaches basic concepts such as sequence data, recurrent neural networks (RNN), LSTM, GRU, and transformers. Participants learn how to use pre-trained models to solve various problems such as sentiment analysis, text summarization, and question answering. The course is organized into four chapters: Chapter 1 introduces RNNs and building simple models with Keras. Chapter 2 covers problems such as vanishing gradients and their solutions including LSTM and GRU. Chapter 3 is dedicated to transformer architecture, attention mechanisms, and transfer learning. Chapter 4 shows the practical application of transformer models in various NLP tasks. Each chapter ends with a short quiz and finally, guidance is provided for continuing the learning path.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n