A Neural Network in 11 lines of Python (2015)
a year ago
- #backpropagation
- #python
- #neural-networks
- The tutorial introduces backpropagation through a simple Python implementation using toy code.
- A 2-layer neural network is demonstrated with input and output datasets, using numpy for matrix operations.
- The sigmoid function is used as a nonlinearity to convert numbers to probabilities between 0 and 1.
- Training involves forward propagation, error calculation, and weight updates via gradient descent.
- A 3-layer neural network is introduced to handle nonlinear patterns by combining inputs in hidden layers.
- Key concepts include matrix multiplication, error backpropagation, and weight initialization.
- Future improvements suggested include adding bias units, mini-batches, regularization, and dropout.