Artificial neural networks or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules. Link - https://towardsdatascience.com/understanding-neural-networks-19020b758230
Basic implementation of Neural Network to predict House price with the help of Keras Library
- pandas
- from keras.models import Sequential
- from keras.layers import Dense
- from sklearn.model_selection import train_test_split
- matplotlib
Keras contains numerous implementations of commonly used neural-network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools to make working with image and text data easier to simplify the coding necessary for writing deep neural network code. The code is hosted on GitHub, and community support forums include the GitHub issues page, and a Slack channel. Documentation - https://keras.io/api/