What you will learn
- Understand the principles and applications of deep learning
- Design and train neural networks for various tasks
- Implement convolutional and recurrent neural networks
- Explore unsupervised learning with autoencoders and GANs
- Apply deep reinforcement learning for decision-making tasks
- Master transfer learning and fine-tuning techniques
- Interpret and explain deep learning models
Beneficial for
- Data Scientists
- Machine Learning Engineers
- Researchers in Artificial Intelligence
- Software Developers interested in deep learning
- Anyone seeking a comprehensive understanding of deep learning concepts and applications
Course Pre-requisite
- Basic understanding of machine learning concepts
- Familiarity with programming (preferably Python)
- Enthusiasm for exploring and applying deep learning techniques is key.
Course Outline
Understanding the fundamentals of deep learning
Overview of neural networks and their applications
Historical development and milestones in deep learning
Architecture and components of a basic neural network
Activation functions and their role in neural networks
Backpropagation algorithm for training neural networks
Introduction to convolutional layers in deep learning
Designing and training CNNs for image recognition tasks
Transfer learning with pre-trained CNNs
Understanding recurrent layers in deep learning
Building and training RNNs for sequential data
Applications of RNNs in natural language processing and time-series analysis
Principles and applications of autoencoders
Implementing unsupervised learning with autoencoders
Generative models and their role in unsupervised learning
Introduction to GANs and their architecture
Generating realistic images with GANs
Applications of GANs in image synthesis and data augmentation
Basics of reinforcement learning and deep Q-networks (DQNs)
Training agents for decision-making using deep reinforcement learning
Applications of deep reinforcement learning in gaming and robotics
Strategies for transfer learning in deep learning
Fine-tuning pre-trained models for specific tasks
Implementing transfer learning in practical scenarios
Challenges and importance of interpretability in deep learning
Techniques for interpreting and explaining deep learning models
Balancing accuracy and interpretability in model design
Understanding ethical challenges in deep learning
Bias and fairness in machine learning models
Responsible AI practices and considerations
