Implementing neural networks with NumPy

While NumPy is definitely not the go-to package for training a neural network in real-time scenarios, learning to implement it in NumPy brings out the flexibility and might of NumPy for doing complex matrix computations and also provides a better understanding of neural networks.

First, let's synthetically generate a dataset for a binary classification problem that will be used for training the neural network. The data will be from two different Gaussian distributions, and the model will be trained to classify this data into either of the two categories. Let's generate the data with 1,000 samples in each category:

N = 1000X1 = np.random.randn(N, 2) + np.array([0.9, 0.9])X2 = np.random.randn(N, 2) + ...

Get Mastering pandas - Second Edition now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.