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Starts 9 June 2025 01:42
Ends 9 June 2025
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What We Need to Do to Continue Advancing Deep Learning
Explore advancements in deep learning, including reinforcement learning, generative adversarial networks, and neural networks, with insights on AI's future and its impact on various fields.
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Overview
Explore advancements in deep learning, including reinforcement learning, generative adversarial networks, and neural networks, with insights on AI's future and its impact on various fields.
Syllabus
- Introduction to Deep Learning
- Neural Networks
- Reinforcement Learning
- Generative Adversarial Networks (GANs)
- AI in Various Fields
- Ethical and Societal Implications
- Future Directions in AI and Deep Learning
- Course Conclusion
Overview of Deep Learning
Historical Context and Milestones
Basics of Neural Networks
Advanced Architectures (e.g., CNN, RNN, Transformers)
Applications in Image and Speech Recognition
Fundamentals of Reinforcement Learning
Key Algorithms (e.g., Q-Learning, Deep Q-Networks)
Real-world Applications and Challenges
Introduction to GANs
Variants and Improvements (e.g., DCGAN, StyleGAN)
Use Cases and Innovations
Healthcare and Medical Diagnostics
Autonomous Vehicles
Finance and Algorithmic Trading
Natural Language Processing
Bias and Fairness in AI
Privacy Concerns
Job Displacement and Economic Impact
Emerging Trends
Key Research Areas
Industry Collaboration and Open Challenges
Summary of Key Learnings
Discussion on the Future of AI and Deep Learning
Subjects
Conference Talks
