Deep Learning Architectures
Mastering Advanced Deep Learning Architectures for Cutting-Edge AI Research
About This Course
This three-day workshop delves into advanced neural network architectures, including Residual Networks (ResNets), DenseNets, EfficientNet, NasNet, LSTM, GRUs, and Transformers. Participants will engage in hands-on sessions and case studies to understand the practical applications of these architectures.
Aim
To provide PhD scholars and academicians with advanced skills in designing and optimizing deep learning architectures. This course aims to enhance understanding of advanced neural networks, CNNs, and RNNs, focusing on their applications and optimization techniques.
Workshop Objectives
- Master advanced neural network architectures.
- Implement state-of-the-art CNN advancements.
- Develop and optimize advanced RNN models.
- Apply deep learning architectures to real-world problems.
- Enhance research and practical implementation skills.
Workshop Structure
Day 1: Advanced Neural Networks
- Lecture Topics:
- Architectures beyond feedforward networks: Residual Networks (ResNets), DenseNets
- Optimization techniques and activation functions
- Discussion & Case Studies:
- Research insights on optimizing deep networks
- Interactive session on designing custom architectures
Day 2: Advanced Convolutional Neural Networks (CNNs)
- Lecture Topics:
- Latest advancements in CNNs: EfficientNet, NasNet
- Transfer learning and fine-tuning pre-trained models
- Discussion & Case Studies:
- Applications in medical imaging and autonomous vehicles
- Practical implementation and best practices
Day 3: Advanced Recurrent Neural Networks (RNNs)
- Lecture Topics:
- Beyond basic RNNs: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs)
- Attention mechanisms and Transformers
- Discussion & Case Studies:
- Case studies in natural language generation and time-series forecasting
- Hands-on session with sequential data
Who Should Enrol?
Data scientists, machine learning engineers, AI researchers, and academicians in AI and machine learning.
Important Dates
Registration Ends
09/17/2024
IST 1:00 pm
Workshop Dates
09/17/2024 – 09/19/2024
IST 5:00 PM
Workshop Outcomes
- Design and optimize advanced neural network architectures.
- Implement and fine-tune state-of-the-art CNN models.
- Develop advanced RNN models with attention mechanisms.
- Apply deep learning techniques to practical applications in various fields.
- Conduct high-level research in deep learning and AI.
Fee Structure
Student Standard fee
₹1499 | $40
Ph.D. Scholar / Researcher Standard fee
₹1999 | $45
Academician / Faculty Standard fee
₹2999 | $50
Industry Professional Standard fee
₹4999 | $75
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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