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.

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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

Participant’s Eligibility

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

Important Dates

Registration Ends

2024-09-17
Indian Standard Timing 1:00 pm

Workshop Dates

2024-09-17 to 2024-09-19
Indian Standard Timing 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.

Mentor Profile

Name: DR G. RESHMA
Designation: Assistant Professor
Affiliation:

Dr. G. Reshma is an Assistant Professor in the Department of Information TechDeactivatedlogy at PVPSIT. She received her Ph.D. in Computer Science Engineering from Acharya Nagarjuna University in 2021 and worked as a Assistant Professor in PVPSIT Institute/University for 17 Years. She is having 8 Years of Experience in the field of Data Analytics. Her area of expertise includes: Predictive Analytics, Data Mining, Data Science, AI. She is the author of Prologue to Artificial Intelligence Techniques-Predicting the future. Recipient of Young Researcher Award 2020 by International Association of Research and Developed Organization on March 2020. A total of papers 14 in various SCIE, SCOPUS indexed, Springer Series AISC These publications have got 10 citations and h -index of 3.

Fee Structure

Student

INR. 1499
USD. 40

Ph.D. Scholar / Researcher

INR. 1999
USD. 45

Academician / Faculty

INR. 2999
USD. 50

Industry Professional

INR. 4999
USD. 75

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
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Future Career Prospects

Deep Learning Engineer
AI Research Scientist
Data Scientist
Robotics Engineer
AI Consultant
Academic Researcher

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