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:
- From Neural Networks to Deep Learning
- Best Practice to Develop a Deep Learning Model
- Deep Learning Implementation and Experimentation in Practice
- Convolutional Neural Networks for Complex Computer Vision Tasks
- Implementing a Convolutional Neural Network Model
- Representation Learning with Neural Embedding
- Advanced Convolutional Neural Networks
- Deep Learning for Natural Language Processing: Recurrent Neural Network and Transformer
- Implementing a Deep Learning Model for Text Classification
- Multimodal Deep Learning
- The Future of Deep Learning
- Mathematical Background of Deep Learning
- Deep Learning for Computer Vision: Convolutional Neural Network
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