Deep Learning for Academic Research

1. Introduction to Deep Learning
   • Deep Learning vs. Machine Learning
   • Overview of neural networks: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
2. Deep Learning Frameworks
   • Setting up TensorFlow, Keras, and PyTorch for research
   • Building and training neural networks
3. Convolutional Neural Networks (CNNs)
   • Applications of CNNs in image processing and research
   • Practical hands-on: Building CNN models
4. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
   • Applications in time-series data and sequence prediction
5. 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

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