artificial neural networks

Training, saving and loading Artificial Neural Networks in Keras

We demonstrate how to code a Artificial neural network model and train and save it in JSON or H5 format which can be loaded later for any inference task. We use Keras/ TensorFlow to demonstrate this transfer learning and used Pima Indian Diabetes dataset in CSV format

Machine Learning (ML)

Convolutional Neural Networks (CNN)

Convolutional Neural Network (CNN) is an neural network which extracts or identifies a feature in a particular image and is the basis of GoogleNet and VGG19 and used for object detection and classification. CNN has five basic components Convolution, ReLU, Pooling, Flattening and Full connection.

Machine Learning (ML)

Feed Forward Neural Networks

A feedforward neural network is an Artificial Neural Network in which connections between the nodes do not form a cycle. Learn about how it uses ReLU and other activation functions, perceptrons, early stopping, overfitting, and others. See the architecture of various Feed Forward Neural Networks

Machine Learning (ML)

Types of Neural Network optimizations

The types of neural network optimizations are weight pruning, structured pruning, convolution, fully-connected, structured group, structure ranking with activations like Lp norm, block pruning, model thinning, compression schedule, regularization, group lasso, group variance, quantization and others

Machine Learning (ML)

An Artificial Neural Network is a form of computing system that vaguely resembles the biological nervous system. It is composed of very many neurons that are centres of computation and learn by a sort of hit and trial method over the course of many epochs. ANN can be seen as a network of perceptrons