The Deep Learning for Academic Research workshop focuses on the theoretical foundations and practical applications of deep learning in academia. Through hands-on projects and case studies, participants will gain expertise in leveraging neural networks, advanced architectures, and data-driven methodologies to enhance their research outputs. The workshop is tailored for academicians and PhD scholars aiming to integrate deep learning into their research workflows.

Aim

This workshop equips researchers and academicians with in-depth knowledge of deep learning techniques, emphasizing their applications in academic research. Participants will learn to design, implement, and analyze deep learning models for solving complex research problems across disciplines.

Enroll
Batches
Explore

Workshop Objectives

Provide a comprehensive understanding of deep learning concepts and frameworks.
Equip participants with practical skills for implementing deep learning in academic research.
Teach data preprocessing, model optimization, and ethical AI practices.
Enable participants to use deep learning tools for publication-ready research.
Develop a capstone project demonstrating real-world application of deep learning in research.

Workshop Structure

Introduction to Deep Learning
- Deep Learning vs. Machine Learning
- Overview of neural networks: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
Deep Learning Frameworks
- Setting up TensorFlow, Keras, and PyTorch for research
- Building and training neural networks
Convolutional Neural Networks (CNNs)
- Applications of CNNs in image processing and research
- Practical hands-on: Building CNN models
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
- Applications in time-series data and sequence prediction
Model Evaluation and Tuning in Deep Learning
- Model evaluation metrics for deep learning
- Tuning deep learning models for improved performance

Day wise Schedule:

Day 1: Introduction to Deep Learning and Frameworks
- Setting up TensorFlow and Keras for research
- Introduction to neural networks
Day 2: Building Convolutional Neural Networks (CNNs)
- Practical session: Building and training CNNs on research datasets
Day 3: Recurrent Neural Networks (RNNs) and LSTM
- Hands-on session: Applying RNNs and LSTM to time-series data
Day 4: Deep Learning Model Evaluation and Optimization
- Practical: Tuning and evaluating deep learning models for research

Participant’s Eligibility

Academicians, researchers, and PhD scholars across various disciplines such as engineering, healthcare, social sciences, and natural sciences.

Important Dates

Registration Ends

2024-12-19
Indian Standard Timing 1:00 pm

Workshop Dates

2024-12-19 to 2024-12-22
Indian Standard Timing 5 PM

Workshop Outcomes

Mastery of deep learning techniques tailored for academic research.
Practical experience with frameworks like TensorFlow and PyTorch.
Knowledge of advanced architectures such as CNNs, RNNs, and transformers.
Ability to develop reproducible and ethical deep learning research projects.

Mentor Profile

Name: Dr. Galiveeti Poornima
Designation: Assistant Professor
Affiliation: Presidency University, Bengaluru, SJCIT , Chikkabalapur, IIITB, Bengaluru

Dr. Galiveeti Poornima is a distinguished academician and researcher specializing in Machine Learning (ML) and Deep Learning. With a Ph.D. in Computer Science from Presidency University, Bengaluru, she has devoted her research to pioneering advancements in Signed Language Recognition, particularly for Indian Sign Languages. Her expertise extends to the application of ML and AI in healthcare, IoT, and cybersecurity, where she has contributed significantly to the development of intelligent systems for medical diagnostics and social media analytics.

Fee Structure

Student

INR. 1999
USD. 55

Ph.D. Scholar / Researcher

INR. 2599
USD. 60

Academician / Faculty

INR. 3999
USD. 70

Industry Professional

INR. 6499
USD. 100

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!
List of Currencies

FOR QUERIES, FEEDBACK OR ASSISTANCE

Contact Learner Support

Best of support with us

Phone (For Voice Call)