Advanced CNNs, Transfer Learning, and Recurrent Networks

About this course

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Embark on a journey through the intricate workings of advanced Convolutional Neural Networks (CNNs), Transfer Learning, and Recurrent Neural Networks (RNNs). This course begins with a thorough exploration of CNNs, delving into sophisticated architectures like VGG16 and practical applications through multi-part case studies. Each segment is designed to build your foundational knowledge and practical skills incrementally. Transitioning into Transfer Learning, the course explores pivotal models such as AlexNet, GoogleNet, and ResNet. You will engage with numerous hands-on sessions, applying transfer learning techniques to real-world datasets. These sessions are meticulously crafted to ensure a robust understanding of how pre-trained models can accelerate your projects and improve outcomes. The course culminates with an in-depth study of Recurrent Neural Networks, including Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). By working through comprehensive case studies, you'll gain practical experience in applying RNNs to sequential data tasks such as part-of-speech tagging and text generation. Each module is designed to provide a seamless learning experience, combining theoretical insights with practical implementation. This course is tailored for data scientists, machine learning engineers, and AI enthusiasts with a solid understanding of basic neural networks and Python programming. Prerequisites include prior experience with deep learning frameworks such as TensorFlow or Keras, and familiarity with fundamental machine learning concepts.

Course objectives

• build a solid foundation in advanced neural networks
• gain practical skills through case studies
• develop expertise in real-world applications of deep learning