Training the Network
Let’s try out the new
.train() method. You can create a 2D NumPy array of input vectors and then a single vector of targets. Create a new instance of the
NeuralNetwork class and set the learning rate to
01:25 There isn’t much for the network to sink its teeth into. Also, you’re testing the dataset using the same data it was trained on, which can lead to overfitting. A real-world deep neural network would be designed to work with much larger datasets, and also it would have many more layers. As layers and activation functions are added to the network, it increases the accuracy of the predictions generated from the trained model.
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