دوره آموزشی یادگیری عمیق

1. Introduction to Deep Learning: Basic concepts, history, and applications

2. Fundamentals of Deep Learning: Neural networks, deep learning algorithms, optimization techniques

3. Deep Learning Frameworks and Tools: TensorFlow, Keras, PyTorch, etc.

4. Convolutional Neural Networks (CNNs): Architecture, training, and applications in image processing

5. Recurrent Neural Networks (RNNs): Architecture, training, and applications in natural language processing

6. Generative Adversarial Networks (GANs): Architecture, training, and applications in image generation

7. Transfer Learning and Fine-tuning: Techniques for leveraging pre-trained models for new tasks

8. Deep Reinforcement Learning: Basics of reinforcement learning and its application in deep learning

9. Practical Applications of Deep Learning: Medical diagnosis, autonomous vehicles, marketing analytics, etc.

10. Hands-on Projects: Implementing deep learning algorithms for real-world problems

11. Future Trends in Deep Learning: New developments and potential applications in the field