Workshop Registration End Date :17 Sep 2024

ML2
Virtual Workshop

Deep Learning Architectures

Mastering Advanced Deep Learning Architectures for Cutting-Edge AI Research

Skills you will gain:

About Workshop:

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.

What you will learn?

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

Mentor Profile

DR G. RESHMA Assistant Professor
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Fee Plan

StudentINR 1499/- OR USD 40
Ph.D. Scholar / ResearcherINR 1999/- OR USD 45
Academician / FacultyINR 2999/- OR USD 50
Industry ProfessionalINR 4999/- OR USD 75

Important Dates

Registration Ends
17 Sep 2024 Indian Standard Timing 1:00 pm
Workshop Dates
17 Sep 2024 to
19 Sep 2024  Indian Standard Timing 5:00 PM

Get an e-Certificate of Participation!

2024Certfiacte

Intended For :

Data scientists, machine learning engineers, AI researchers, and academicians in AI and machine learning.

Career Supporting Skills

ResNets DenseNets EfficientNet NasNet LSTM GRUs Transformers

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.