Industry Use Cases On Neural Networks

What are neural networks?

A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus a neural network is either a biological neural network, made up of real biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled as weights. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be ?1 and 1.

These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset. Self-learning resulting from experience can occur within networks, which can derive conclusions from a complex and seemingly unrelated set of information.

What Exactly Are Neural Networks?

• Neural networks are a programming approach that is inspired by the neurons in the human brain and that enables computers to learn from observational data, be it images, audio, text, labels, strings or numbers. They try to model some unknown function (for example, ) that maps this data to numbers or classes by recognizing patterns in the data. They are built from these components:
• Encoders and Decoders (to convert the input data type to numeric tensors)
• Layers (to perform operations on these tensors, depending on the applications)
• Containers (to hold these operations in a sensible way)

Once the neural network is built from these components, it needs to be trained (in other words, optimized).

As you might have guessed, the optimization (or minimizing the “loss” of the network) is done through stochastic gradient descent in an iterative fashion. The inputs are fed to the net repeatedly; the error/loss is computed each time and is then used to update the model’s parameters using back propagation. Back propagation, or “back error propagation,” involves distributing the error computed during forward propagation back to the network’s layers.

Understanding Encoders and Decoders

The input data provided in any form needs to be converted to numeric tensors. Here are a few examples of tensors and their corresponding ranks (dimensions):

• Rank 0 (scalars): 0.0
• Rank 1 (vectors): {0.0, 1.0}
• Rank 2 (matrices): {{1.,2.,3.} , {3., 2., 1.}}
• Rank-n tensors: {… {… {1., 2., 3.}…}…}

Applying layers

Using Containers :-

Machine Learning Model Considerations

1. Handoff from Data Scientists to DevOps
2. Portability
3. Scalability
4. Ease of Model Refreshing

Preparing a machine learning model for production work requires taking many factors into account. The needs include an easy method to hand off data between the Data Science team and the DevOps team. The model can be quickly deployed by making it portable, which allows for easy updates. It should be scalable so that as usage increases it is easy to scale. The model also needs an established cadence for Model Refreshing, and an easy way to roll back to previous versions of the model.

The typical output of a Neural Network training is a serialized model and training values for that model. The model is deserialized and the training is applied to prepare it for use. We can store this model and training data in a separate docker container from the service that is actually performing the prediction using the model. This allows each to version separately, opening up a lot of possibilities. It allows for easy updates to either the model or the service consuming the model independently. Containerization also opens up the possibility of using Kubernetes for scale or to push the container via Azure IoT Edge to all of your Edge devices. It also allows for the possibility of A/B testing your models to see which performs better with your customer base.

Use Cases Of Neural Networks :-

Reducing Email Fatigue and Improving Conversion Rates

By only advertising relevant products to interested customers, you also reduce the chances of customers developing email fatigue. In short, if your advertisements are relevant and interesting customers are more likely to interact. This drives visits to your website, potentially increasing sales, and helps you to build a strong client-business relationship. According to dragon360.com 61% of customers say that they are most likely to use companies that send them targeted content. Applying Artificial Neural Networks in your marketing strategy can save your company both time and money. By streamlining your marketing approach in this way you will only be targeting the customers most likely to purchase your product.

This streamlined approach of targeting the people most likely to engage can help to increase sales and profits. Many companies who have adopted targeted or personalized marketing strategies have noticed clear, positive results. For example, stationery retailers paper style segmented their subscribers into two different groups. Each group then received targeted emails. Consequently, the business reported an open rate increase of 244%. The traffic driven from emails to the website also increased by 161%. These statistics show that personalized marketing campaigns can deliver real results, benefiting businesses.

Neural Networks in the Retail Sector :-

As we have noted, Artificial Neural Networks are versatile systems, capable of dealing reliably with a number of different factors. This ability to handle a number of variables makes Artificial Neural Networks an ideal choice for the retail sector. For instance, Artificial Neural Networks are, when given the right information, able to make accurate forecasts. These forecasts are often more accurate than those made in the traditional manner, by analyzing statistics. This can allow accurate sales forecasts to be generated. In turn, this information allows your businesses to purchase the right amount of stock. This reduces the chances of selling out of certain items. It also reduces the risk of valuable warehouse space being taken up by products you are unable to sell. Online grocers Ocado are making the most of this technology. Their smart warehouses rely on robots to do everything from stock management to fulfilling customer orders.

This information is used to power the trend of dynamic pricing. Many companies, such as Amazon, use dynamic pricing to reproduced and increase revenue. This application has spread beyond retail, service providers, such as Uber, even use this information to adjust prices depending on the customer. Many retail organizations, such as Walmart, use Artificial Neural Networks to predict future product demand. The network models analyze location, historical data sets, as well as weather forecasts, models and other pieces of relevant information. This is used to predict an increase in sales of umbrellas or snow clearing products. By predicting a potential rise in demand the company is able to increase stock in store. This means that customers won’t leave empty-handed and also allows Walmart to offer product-related offers and incentives.

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