People Innovation Excellence

DEEP LEARNING (2 Credits)

Learning Outcomes:

On successful completion of this course, student will be able to: Explain the fundamental deep learning theory; Execute a proper deep learning experimentation workflow; Analyze a theoretical deep learning model; Compose a deep learning code.

Topics:

  1. From Neural Networks to Deep Learning
  2. Best Practice to Develop a Deep Learning Model
  3. Deep Learning Implementation and Experimentation in Practice
  4. Convolutional Neural Networks for Complex Computer Vision Tasks
  5. Implementing a Convolutional Neural Network Model
  6. Representation Learning with Neural Embedding
  7. Advanced Convolutional Neural Networks
  8. Deep Learning for Natural Language Processing: Recurrent Neural Network and Transformer
  9. Implementing a Deep Learning Model for Text Classification
  10. Multimodal Deep Learning
  11. The Future of Deep Learning
  12. Mathematical Background of Deep Learning
  13. Deep Learning for Computer Vision: Convolutional Neural Network

Published at : Updated

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