LinkedIn – Deep Learning with Python: Sequence Models and Transformers 2025-4

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.

What you will learn

• Familiarity with what sequence data is and common applications of sequence models.
• Understand recurrent neural networks (RNNs) and how to build a simple RNN with Keras.
• Familiarity with vanishing and exploding gradient problems and their solutions.
• Understand long short term memory networks (LSTMs) and gated recurrent units (GRUs) and how to choose between them.
• Introduction to the concept of attention and the architecture of the Transformer model.
• Learning the concept of transfer learning and using the Hugging Face Hub.
• Practical application of pre-trained transformer models in Python for tasks such as named entity recognition, sentiment analysis, text summarization, and question answering.

This course is suitable for people who:

• Those interested in deep learning and natural language processing.
• Python developers looking to expand their skills in working with sequence data and transformer models.
• People interested in time series analysis and other applications of sequential data.
• Students and researchers in the fields of artificial intelligence and computer science.

Course details: Deep Learning with Python: Sequence Models and Transformers

• Publisher: LinkedIn
• Instructor: Frederick Nwanganga
• Education level: Intermediate
• Training duration: 1 hour and 26 minutes

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