To solve use cases effectively, we need to use the right neural network architecture by determining the problem type. The following are globally some use cases and respective problem types to consider for step 1:

• Fraud detection problems: We want to differentiate between legitimate and suspicious transactions so as to separate unusual activities from the entire activity list. The intent is to reduce false-positive (that is, incorrectly tagging legitimate transactions as fraud) cases. Hence, this is an anomaly detection problem. 
• Prediction problems: Prediction problems can be classification or regression problems. For labeled classified data, we can have discrete labels. We need to model data against those discrete labels. On the other hand, regression models don't have discrete labels. 
• Recommendation problems: You would need to build a recommender system (a recommendation engine) to recommend products or content to customers. Recommendation engines can also be applied to an agent performing tasks such as gaming, autonomous driving, robotic movements, and more. Recommendation engines implement reinforcement learning and can be enhanced further by introducing deep learning into it. 

We also need to know the type of data that is consumed by the neural network. Here are some use cases and respective data types for step 2:

• Fraud detection problems: Transactions usually happen over a number of time steps. So, we need to continuously collect transaction data over time. This is an example of time series data. Each time sequence represents a new transaction sequence. These time sequences can be regular or irregular. For instance, if you have credit card transaction data to analyze, then you have labeled data. You can also have unlabeled data in the case of user metadata from production logs. We can have supervised/unsupervised datasets for fraud detection analysis, for example. Take a look at the following CSV supervised dataset:

In the preceding screenshot, features such as amount, oldBalanceOrg, and so on make sense and each record has a label indicating whether the particular observation is fraudulent or not.

On the other hand, an unsupervised dataset will not give you any clue about input features. It doesn't have any labels either, as shown in the following CSV data:

As you can see, the feature labels (top row) follow a numbered naming convention without any clue as to its significance for fraud detection outcomes. We can also have time series data where transactions are logged over a series of time steps. 

• Prediction problems: Historical data collected from organizations can be used to train neural networks. These are usually simple file types such as a CSV/text files. Data can be obtained as records. For a stock market prediction problem, the data type would be a time series. A dog breed prediction problem requires feeding in dog images for network training. Stock price prediction is an example of a regression problem. Stock price datasets usually are time series data where stock prices are measured over a series as follows:

In most stock price datasets, there are multiple files. Each one of them represents a company stock market. And each file will have stock prices recorded over a series of time steps, as shown here:

• Recommendation problems: For a product recommendation system, explicit data might be customer reviews posted on a website and implicit data might be the customer activity history, such as product search or purchase history. We will use unlabeled data to feed the neural network. Recommender systems can also solve games or learn a job that requires skills. Agents (trained to perform tasks during reinforcement learning) can take real-time data in the form of image frames or any text data (unsupervised) to learn what actions to make depending on their states.