In an increasingly global market, online retailers need to implement the latest IT solutions to reach their target market and increase sales. And integrating digital technologies into all areas of your e-commerce business will help you stay ahead of your competitors. Here, we’ll take a look at five components of this digital transformation:

As the owner or manager of an e-commerce business, you likely already know how important technology is in today’s market. The brick-and-mortar stores of yesterday are quickly becoming a thing of the past and more people are shopping online than ever before. On top of that, digital technology can also help your business run smoother and with fewer errors.

As the owner or manager of an e-commerce business, you likely already know how important technology is in today’s market. The brick-and-mortar stores of yesterday are quickly becoming a thing of the past and more people are shopping online than ever before. On top of that, digital technology can also help your business run smoother and with fewer errors.

A problem with training neural networks is in the selection of the number of training epochs to use. A lot of epochs can cause overfitting of the training dataset, while too few might have the outcome of an underfit problem. Early stopping is a strategy that facilitates you to mention an arbitrary large number of training epochs and stop training after the model performance ceases improving on a hold out validation dataset.

One critical step within machine learning is the selection of model. An apt model with relevant hyperparameter is the foundation to a good forecasting outcome. When we are encountered with a selection between models, how should the decision be made?

With regards to sequential or time series data, conventional feedforward networks can’t be leveraged for learning and forecasting/prediction. A mechanism is needed that can retain historical data to predict the future values. Recurrent neural networks or RNNs in short are a variety of the traditional feedforward artificial neural networks that can handle sequential data and can be trained to retain the know-how, from a historical perspective.

The Generative Adversarial Network, or GAN for short, is an architecture for training of a generative model. The architecture is consisted of dual models. The generator that we are concerned with, and a discriminator model that is leveraged to help in the training of the generator. To start with, both of the generator and discriminator models were implemented as Multilayer Perceptrons (MLP), even though more lately, the models are implemented as deep convolutional neural networks.

The Wasserstein Generative Adversarial Network, or Wasserstein GAN is an extension to the generative adversarial network (GAN) that both enhances the stability during training of the model and furnishes a loss function that corresponds with the quality of produced imagery.

Human activity recognition, or HAR in short, is a difficult time series classification activity. It consists of forecasting the movement of an individual on the basis of sensor information and conventionally consists of deep domain expertise and strategies that range from the raw data in order to go about fitting a machine learning model. Lately, deep learning strategies like convolutional neural networks and recurrent neural networks have demonstrated potent and even accomplish cutting-edge outcomes by automatically learning features from the

There are several perspectives on what the difference is between a vulnerability assessment versus a penetration test. The primary distinction, appears to be that many hold the belief a comprehensive penetration test consists of identification of as many vulnerabilities as feasible, while others hold the belief that Penetration Tests are objective-oriented and primarily don’t concern themselves with other vulnerabilities might exist.