Learn to create Deep Learning models in Python from two Machine Learning, Data Science experts. Code templates included.
What you’ll learn
- Understand the intuition behind Artificial Neural Networks
- Apply Artificial Neural Networks in practice
- Understand the intuition behind Convolutional Neural Networks
- Apply Convolutional Neural Networks in practice
- Understand the intuition behind Recurrent Neural Networks
- Apply Recurrent Neural Networks in practice
- Understand the intuition behind Self-Organizing Maps
- Apply Self-Organizing Maps in practice
- Understand the intuition behind Boltzmann Machines
- Apply Boltzmann Machines in practice
- Understand the intuition behind AutoEncoders
- Apply AutoEncoders in practice
This course includes:
- 22 hours on-demand video
- 34 articles
- Access on mobile and TV
- Certificate of completion
Key Highlights of the Course:
Comprehensive Curriculum: The course covers a wide array of topics, including:
- Artificial Neural Networks (ANNs)
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Self-Organizing Maps (SOMs)
- Boltzmann MachinesAutoencoders
Hands-On Projects: Learners engage in practical applications such as:
- Building AI-powered chat systems using ChatGPT for automated customer interactions.
- Developing software solutions that integrate transfer learning to enhance business workflows.
- Applying deep learning models to real-world challenges in fields like engineering simulations and seismic imaging.
