🧠 Neural Computation Notes
Contents
- 01 What is Neural Computation ?
- 02 Neurons
- 03 Perceptron Classifier
- 04 Perceptron Regressor
- 05 Optimisation Problem
- 06 Maximum Likelihood
- 07 Gradient Descent
- 08 Multi-Layer Perceptron (MLP) Architecture
- 09 Computation Graphs
- 10 The Back-propagation Algorithm
- 11 Training and Loss Function
- 12 Generalisation
- 13 Training Protocols & Ethics
- 14 Activation Functions
- 15 Digital Image Representation and MLP Limitations
- 16 Convolutional Neural Network
- 17 CNN Structure 4D Data
- 18 Training Optimisation and Normalisation
- 19 Regularisation & Transfer Learning
- 20 Dense Prediction & UNet Architecture
- 21 Supervised vs. Unsupervised Learning
- 22 Auto-Encoders
- 23 Contrastive Learning and SimCLR
- 24 Generative Models and GANs
- 25 Diffusion Models