What Is Deep Learning?

Deep learning is a subset of machine learning. It allows computers to learn from and see patterns and meanings in data. 

With the increasing availability of data, ease of access to data, and advancement of computer technologies, deep learning has become a popular approach to machine learning.

Deep learning models are referred to as deep neural networks. This is due to hidden layers (layers between the input and output). Traditional neural networks have 2-3 hidden layers, while deep models have as many as 150. As such, deep learning models are more computationally heavy than traditional models. Also, deep learning models can solve problems too hard for regular models. For example, deep learning systems can detect objects in images. 

Deep layer models have three layers: input, hidden, and output. Input layers take in data and layer on top of each other to build the model. Hidden layers are where most of the computation takes place. Hidden layers connect to every other layer. Output layers take in a prediction and provide it to the next layer.

Types of Deep Learning Models

There are several types of deep learning models. Some of the most common ones include feedforward neural networks (FNN), convolutional neural networks (CNN), recurrent neural networks (RNN) and autoencoders.

In a feedforward neural network, information is first passed through one or more layers before moving on to the next layer. In this way, information flows forward from input/output to hidden/output. There is no backward propagation involved.

FNNs can recognize patterns in large amounts of data. However, they are hard to train. FNNs are smaller than other types of deep learning models. The reason is that there is backpropagation involved in training them.

How Does Deep Learning Work?

Deep learning is a complex process that involves several steps. These steps are data preparation, model training and deployment. 

Data Preparation

Data preparation cleans and transforms raw data. This is necessary to format the information in a way the model can understand. This step includes preprocessing, feature engineering, and labeling. 

• • Preprocessing: This step removes noise and other artifacts from the data set. It also involves transforming categorical variables into numerical ones.
    
    

• • Feature Engineering: This step involves identifying the data’s key features. Features may include dimensions (for example, the number of products sold). They may also include values (like price). Or, they may include specific content (like product descriptions).
    
    

• • Labeling: This step involves assigning labels to each data point. Labeling helps the model learn how to classify new data points. The process works based on their similarity with existing samples.
    
    

Model Training

Deep learning models are trained by feeding them examples from the dataset and showing how to solve the problem. An example of this is building a model to recognize handwritten numbers. Training the model involves feeding it images of handwritten numbers. The correct answers are also fed to the model. The more data the model receives, the more accurate it becomes.

Deep learning models are trained using supervised learning algorithms. There are two types of supervised learning algorithms:

• • Classification: Classification predicts a class label. One example is a model that classifies an email as either “spam” or “not spam.”
    
    
  • Regression: Regression predicts a numerical value such as price.
    
    

Deployment

The last step is making the model available for everyday use. Once the model is available, teams can use the model in analytics or visualization platforms.

Testing Deep Learning Models

Testing the model involves feeding it new examples not used during training. Comparing the predictions to the actual values reveals the model’s accuracy. Accuracy is measured using precision and recall. 

• • Precision: The degree of closeness between the value you measure and your target value.
    
    

• • Recall: The system’s ability to remember specific data items.
    
    

Use Cases for Deep Learning

Healthcare

Healthcare companies are increasingly using deep learning for several applications, including:

• • Identifying disease
    
  • Biomarker discovery
    
  • Image processing and analysis
    
  • Clinical trial monitoring and quality control
    
  • Patient safety monitoring and alerts
    
    

Finance

Deep learning can detect fraudulent transactions by finding patterns in large datasets. When a fraudulent transaction is detected, the system can alert the right people so that they can take action.

Marketing

Deep learning algorithms can analyze big data collected from social media platforms to predict what kind of content consumers want to see. Marketing teams can then use this information to create more relevant content and optimize their social media strategy accordingly.

Deep Learning: The Key To Data-Driven Business Strategy

With the rise of big data, organizations are looking for ways to gain a competitive edge and make smarter decisions. Deep learning gives companies insight into their data. Companies rely on this insight to inform business strategy.