Track 1: Deep Learning Fundamentals

  • Deep Neural Networks
  • Deep Neural Networks Optimization Algorithms
  • Deep Feedforward Networks
  • Regularization
  • Deep Convolutional Neural Networks
  • Deep Recurrent Neural Networks
  • Sequence Modelling
  • Loss functions, and training methodologies.
  • Theoretical foundations and mathematical concepts behind Deep Learning
  • Applications of deep learning in various engineering streams

Track 2: Visual Artificial Intelligence

  • Object detection, image segmentation, and image recognition using deep learning.
  • Image Generation and Style Transfer.
  • Blockchain for computer vision/AI provenance
  • Quantum Machine Learning In Computer Vision
  • Visual feature extraction and dimensionality reduction.
  • Computer vision system security using blockchain

Track 3: Natural Language Processing and Multimodal Learning

  • Deep learning for text and speech processing.
  • Combining visual and textual information for improved understanding.
  • Multimodal fusion for applications such as image captioning and sentiment analysis.

Track 4: Deep Reinforcement Learning

  • Deep Q-Networks (DQN) and policy gradient methods.
  • Deep Belief Networks and Statistical Learning
  • Reinforcement learning in robotics and autonomous systems.
  • Applications in game playing and autonomous navigation.

Track 5: Generative Adversarial Networks (GANs)

  • GAN architecture, training, and applications in image synthesis.
  • Conditional GANs and their use in various domains.
  • Deep Generative Models
  • Ethical considerations and challenges of GAN-generated content.

Track 6: Transfer Learning and Pretrained Models

  • Inference Dependencies on Multi-Layered Networks
  • Tensors for Deep Learning
  • Multi-Scale Deep Architecture and Learning
  • Fine-tuning pre-trained models.
  • Knowledge transfer and domain adaptation in deep learning.
  • Practical use cases for transfer learning.

Track 7: Interpretability and Explainability

  • Methods for understanding and interpreting deep learning models.
  • Training schemes, GPU computation, and paradigms
  • Explainable AI (XAI) techniques and their importance in real-world applications.
  • Visualizing and explaining model decisions.

Track 8: Applications of Deep Learning in Healthcare

  • Medical image analysis and diagnosis using deep learning.
  • Predictive modeling for disease diagnosis and patient outcomes.
  • Ethical and regulatory aspects of AI in healthcare.

Track 9: Deep Learning for Autonomous Systems

  • Autonomous Computing
  • Self-driving cars and autonomous drones.
  • Smart cities and intelligent transportation systems.
  • Deep learning in industrial automation and manufacturing.
  • Extreme Learning Machines
  • Hybrid Intelligent Systems

Track 10: Ethical and Societal Implications

  • Discussion of ethical concerns in deep learning and AI.
  • Bias and fairness in AI algorithms.
  • Representation embedding spaces
  • Societal impacts and the responsible use of AI technology.

Track 11: Industry and Business Applications

  • Quantum Deep Learning
  • Case studies and real-world implementations of deep learning.
  • Startups and innovation in deep learning.
  • Deep learning in finance, marketing, and other sectors.

Track 12: Future Trends and Research Directions

  • Emerging trends in deep learning and visual AI.
  • Cross-disciplinary collaborations and future research areas.
  • Applications of deep learning in various engineering streams
  • Challenges and opportunities in the field.