Neural Networks

Neural Networks and Deep Learning

Weights randomly initialized and normalized by He-method, biasses initialized as zeros.
Andrew Ng course: "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization"

Left: Binary dots data. Right: Decision boundary.

Classification algorithm with neural networks of various hidden layers and nodes using logistic regression and gradient descent. Weights randomly initialized, biasses initialized as zeros.
Andrew Ng course: Neural Networks and Deep Learning

Left: Binary flower data. Center: 4 hidden layers. Right: varying number of hidden layers. 5 layers seems to be the best choice for this model and this training data.

Other examples.
Andrew Ng course: Neural Networks and Deep Learning

Left: data. Center: 4 hidden layers. Right: varying number of hidden layers. 5 layers seems to be the best choice for most of these datasets.

Neural Networks and Deep Learning

Weights randomly initialized and normalized by He-method, biasses initialized as zeros. Andrew Ng course: "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization"

Classification algorithm with neural networks of various hidden layers and nodes using logistic regression and gradient descent. Weights randomly initialized, biasses initialized as zeros. Andrew Ng course: Neural Networks and Deep Learning

Other examples. Andrew Ng course: Neural Networks and Deep Learning

Weights randomly initialized and normalized by He-method, biasses initialized as zeros.
Andrew Ng course: "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization"

Classification algorithm with neural networks of various hidden layers and nodes using logistic regression and gradient descent. Weights randomly initialized, biasses initialized as zeros.
Andrew Ng course: Neural Networks and Deep Learning

Other examples.
Andrew Ng course: Neural Networks and Deep Learning