Welcome to the practice projects section for our Advanced Deep Learning course. Here, you will find a variety of projects designed to deepen your understanding and practical skills in deep learning.
Project Overview
Below is a list of practice projects that you can work on. Each project is designed to challenge and expand your knowledge of advanced deep learning concepts.
- Neural Network Architectures: Implement and compare different neural network architectures like CNNs, RNNs, and LSTMs.
- Transfer Learning: Apply pre-trained models to new tasks and fine-tune them for better performance.
- Generative Models: Experiment with generative models like GANs and VAEs to create realistic images or sequences.
- Reinforcement Learning: Build agents that learn to make decisions in complex environments using reinforcement learning techniques.
Project Details
Neural Network Architectures
To get started with neural network architectures, you can begin by implementing a simple CNN for image classification. Once you have a grasp on that, move on to more complex architectures like RNNs and LSTMs for sequence data.
Transfer Learning
Transfer learning is a powerful technique that allows you to leverage the knowledge gained from one problem to another. In this project, you will use a pre-trained model from the TensorFlow Hub and fine-tune it for a new task.
Generative Models
Generative models are fascinating and can produce impressive results. In this project, you will work on implementing a GAN or a VAE to generate realistic images or sequences.
Reinforcement Learning
Reinforcement learning is a field that deals with how agents make decisions in an environment to maximize some notion of cumulative reward. In this project, you will build an agent that learns to play a game using reinforcement learning techniques.
Conclusion
These practice projects are designed to help you gain hands-on experience with advanced deep learning concepts. We hope you find them engaging and informative.