Gain a high-level understanding of how a NN works. Understand main components of NN input data, layers, weights, targets, loss function, optimizers, train and test sets, ...
Breakdown
Introduction and Overview
Brief history of NN
Importance and applications in today's world
Relevance to Arctic science
Basic concepts and terminology
Neurons and layers: Input, Hidden, Output
Weights and biases
Activation functions: Sigmoid, ReLU, etc.
How Neural Networks Learn
Forward propagation: How input becomes output
Cost function: Measuring how "wrong" the network is
Backpropagation: Adjusting weights and biases
Gradient descent and learning rate
Types of Neural Networks
MLP
CNN
RNN/LSTM
Transformer
Training, Validation, and Testing
Splitting data: Why and how
Overfitting and underfitting: Concepts and solutions
Goal
Gain a high-level understanding of how a NN works. Understand main components of NN input data, layers, weights, targets, loss function, optimizers, train and test sets, ...
Breakdown