Deep Learning vs. Neural Network: What’s the Difference?

Deep learning and neural networks coexist in artificial intelligence, with neural networks playing an important role in deep learning. They are essential for deep learning models, which consist of several layers of interconnected neural networks to process and analyze data. Understanding the relationship between deep learning and neural networks is key to unlocking their potential in advanced applications across industries.

Deep learning

Deep learning is a type of machine learning that relies on neural networks to learn from training data. What differentiates deep learning from typical machine learning is that deep learning features several layers of neural networks, specifically three or more. These additional layers make deep learning algorithms superior for certain applications. Areas where deep learning outperforms machine learning are generally those that require especially large data sets, such as natural language processing, computer vision, and speech recognition. 

What is deep learning used for?

Deep learning powers applications and services that automate tasks, enhancing efficiency and accuracy. You can see its influence in everyday technologies, including virtual assistants, fraud detection tools, voice-controlled devices, and autonomous vehicles. Its applications are becoming prominent throughout many types of businesses and organizations, from health care and insurance to finance and technology. Take a look at some of the ways deep learning works within different industries:

• Marketing: Businesses use deep learning to create and implement personalized advertising campaigns. By analyzing data, marketers can learn the types of advertisements customers are more likely to respond to and focus on the most successful strategies.

• Financial services: Deep learning can help mitigate the risk of fraud by detecting suspicious activity and making predictions of future stock values to help inform trading decisions. 

• Health care: Deep learning assists in several ways in the health care industry. Image detection helps doctors identify cancer and diseases early on and provides guidance on treatment plans based on electronic health records.

• Search engines: When you search on the Internet or streaming platforms, search engines can make recommendations and predictions about what you want to watch next or products you may be interested in purchasing based on your online activity. 

• Insurance: Insurance companies use deep learning to estimate claim costs, allowing them to analyze different coverage plan options and spot fraudulent insurance claims by identifying unusual patterns. 

Advantages of deep learning

Deep learning offers several benefits, including requiring very little human intervention. This is because deep learning is capable of unsupervised learning, allowing models to process training data without requiring prior labeling and independently make connections to understand the data. Another advantage of deep learning is the sheer volume of data it can handle. This allows for more accurate outcomes and the ability to recognize unique relationships between the data.

Disadvantages of deep learning

While the ability to handle large amounts of data is an advantage of deep learning, this also presents a shortcoming because it makes deep learning less applicable in situations where data isn’t abundant. Additionally, it’s important to have the right infrastructure in place, with enough computational power to operate efficiently even with the large processing demands required by deep learning algorithms.

Neural network

A neural network, otherwise referred to as an artificial neural network, is a machine learning algorithm that functions similarly to how our brain works, with neurons transmitting information between layers of interconnected nodes. Neural networks contain an input layer, one or more hidden layers, and an output layer. The main processing occurs within the hidden layers, where information is analyzed and assigned a weight, which is the strength of the input. With their human-like ability to learn and become increasingly more accurate over time, you can use neural networks to make decisions, categorize information, and more.

What are neural networks used for?

Various types of neural networks exist, each with the ability to perform certain tasks. Some common types of neural networks and their use cases include:

• Convolutional neural network (CNN): CNNs are primarily used for computer vision tasks, with each layer in the neural network detecting different features within an image. This allows machines to identify and classify information in pictures and videos, such as detecting objects for training self-driving cars or diagnosing medical images.

• Feedforward neural network (FNN): In FNNs, the neurons within the same hidden layer aren’t connected to one another. Instead, data flows directly forward, into the next layer. FNNs can identify complex relationships in data, such as analyzing historical data to make predictions and analyzing patterns within the human language for voice-to-text applications.

• Recurrent neural network (RNN): RNNs process sequential data by storing information from data as it moves through the algorithm to inform current outputs. This capability makes them effective for tasks such as language translation, sentiment analysis, and trend forecasting from historical data. For example, in language translation, RNNs consider the sequence of words to determine the sentiment.

Advantages of neural networks

Neural networks are advantageous because they give computers the power to learn with minimal assistance from humans. As a result, you have access to information that previously was difficult to acquire. This enables you to make informed strategic decisions, develop innovative products, and automate routine tasks.

Disadvantages of neural networks

Some disadvantages of neural networks present themselves within the training phase when neural networks often depend on labeled training data that may not be readily available. You also need to have large data sets for training neural networks.

Potential challenges exist with output as well. For example, poor-quality training data can lead to biased results. Additionally, understanding how neural networks come to their conclusions is unclear so you may not understand why you got a certain output.

Deep learning vs. neural network: other things to consider

When choosing to implement deep learning or neural network models, it’s important to consider the scope of your project and the resources you have access to since deep learning requires more data, and more intensive training, which leads to higher costs. Although deep learning models are more powerful, the simplicity of neural networks compared to deep learning makes them quicker to train and more accessible.

Getting started with deep learning and neural networks on Coursera

Deep learning is a more advanced form of neural networks, utilizing more hidden layers for greater computational power. On Coursera, you can find highly rated courses to learn more about deep learning, and neural networks. Consider Neural Networks and Deep Learning from DeepLearning.AI, which gives you an opportunity to build and train deep neural networks. 

Through Introduction to Deep Learning and Neural Networks with Keras from IBM, you can explore the Keras library to build your own deep learning model, in addition to learning about both supervised and unsupervised deep learning models.