Neurocomputing
Neurocomputing
Introduction
1. Introduction
2. Math basics (optional)
3. Neurons
Linear models
1. Optimization
2. Linear Regression
3. Regularization
4. Linear classification
5. Multi-class classification
6. Learning theory
Deep learning
1. Artificial neural networks
2. Deep neural networks
3. Convolutional neural networks
4. Object detection
5. Semantic segmentation
6. Autoencoders
7. Restricted Boltzmann machines (optional)
8. Generative adversarial networks
9. Recurrent neural networks
10. Attentional neural networks
Neurocomputing
1. Limits of deep learning
2. Hopfield networks
3. Reservoir computing
4. Unsupervised Hebbian learning
5. Spiking neural networks
Exercises
1. Introduction to Python
1.1. Notebook
1.2. Solution
2. Numpy and Matplotlib
2.1. Notebook
2.2. Solution
3. Linear regression
3.1. Notebook
3.2. Solution
4. Multiple Linear Regression
4.1. Notebook
4.2. Solution
5. Cross-validation
5.1. Notebook
5.2. Solution
6. Linear classification
6.1. Notebook
6.2. Solution
7. Softmax classifier
7.1. Notebook
7.2. Solution
8. Multi-layer perceptron
8.1. Notebook
8.2. Solution
9. MNIST classification using keras
9.1. Notebook
9.2. Solution
10. Convolutional neural networks
10.1. Notebook
10.2. Solution
11. Transfer learning
11.1. Notebook
11.2. Solution
12. Variational autoencoder
12.1. Notebook
12.2. Solution
13. Recurrent neural networks
13.1. Notebook
13.2. Solution
References
1. Bibliography
Index
