Course introduces deep learning from an applied perspective in Python with PyTorch. Followed by ECE421 — Introduction to Machine Learning.
Recommended textbooks:
- Dive into Deep Learning, by Zhang, et al.
- Deep Learning, by Goodfellow, et al.
Concepts covered
Neural networks
- Neuron
- Activation function
- Linear activation function
- Unit step function (and sign function)
- Sigmoid
- ReLU
- Softmax
- Temperature scaling
- Neural network layer
- Neural network architectures
- Feed-forward network (MLP)
- Fully-connected network
- Residual network
- Convolutional neural network (CNN)
- Convolution
- Pooling
- Existing architectures
- Transposed convolution
- Autoencoder
- Recurrent neural network (RNN)
- Long short-term memory (LSTM)
- Gated recurrent unit (GRU)
- Sampling strategies
- Greedy search
- Beam search
- Temperature scaling
- Graph neural network (GNN)
- Model performance metrics
- Error function
- Mean-squared error
- Cross entropy (CE) and binary cross entropy (BCE)
- Negative log likelihood (NLL)
- Classification metrics
- Accuracy, precision, recall, F1-score, support
- Error function
- Gradient descent
- Data processing
- Batch normalisation
- Transfer learning
Artificial intelligence
- Symbolic artificial intelligence
- Machine learning
- Types of ML approaches
- Types of ML problems
- Generative AI
- Variational autoencoders (VAE)
- KL divergence
- Generative adversarial network (GAN)
- CycleGAN
- Computer vision
- Natural language processing
- Vector embedding
- Approaches
- Models
- n-gram
- Sentiment analysis
- Vector embedding
- Transformer
- Limits of deep learning
- Explainability
- Adversarial attacks
- Machine learning ethics (racial, gender discrimination)
Tools
- Development environments
- Python libraries
- Numerical computing
- Machine learning
- Visualisation tools
- LaTeX