Target group
Machine learners and those interested in deep learning, computer vision, or natural language processing
Course structure
Introduction to Deep Learning
- Introduction to neural networks.
- Introduction to deep learning.
- ADAM optimization.
- Dropout.
- Batch normalization.
- Autoencoders.
- Building level-specific autoencoders (self-study).
Convolutional Neural Networks
- Applications.
- Input layers.
- Convolutional layers.
- Padding.
- Pooling layers.
- Traditional layers.
- Types of skip-layer connections.
- Image pre-processing and data enrichment.
- Training convolutional neural networks.
Recurrent Neural Networks
- Introduction.
- Recurrent neural networks overview.
- Sub-types of recurrent neural networks.
- Time series analysis using recurrent neural networks.
- Sentiment analysis using recurrent neural networks.
Tuning a Neural Network
- Selecting hyperparameters.
- Hyperband.
Additional Topics
- Types of transfer learning.
- Transfer learning basics.
- Transfer learning strategies.
- Transfer learning with unsupervised pretraining.
- Customizations with FCMP.
