Deep Learning

Detailed syllabus for the Deep Learning course.

• Introduction to Deep Learning
  • Understanding neural networks and deep learning basics
  • Importance and applications of deep learning
• Neural Networks and Backpropagation
  • Building neural networks from scratch
  • Understanding activation functions
  • Backpropagation and gradient descent
• Deep Learning Libraries
  • Introduction to TensorFlow and PyTorch
  • Setting up the environment and writing basic neural networks
• Convolutional Neural Networks (CNNs)
  • Basics of CNNs and their architecture
  • Applications in image processing and recognition
  • Building and training CNN models
• Recurrent Neural Networks (RNNs)
  • Understanding RNNs and sequence modeling
  • Implementing RNNs for time-series and NLP tasks
• Advanced Topics in Deep Learning
  • Transfer learning and pre-trained models
  • Hyperparameter tuning and optimization