The Fundamentals of Deep Learning

Image showing millions of data points slowly dispersing. Fundamentals of deep learning blog post.
Image showing millions of data points slowly dispersing. Fundamentals of deep learning blog post.

We create 2.5 quintillion bytes of data every day. That’s a lot, even when you spread it out across companies and consumers around the world. But it also underscores the fact that in order for all that data to matter, we need to be able to harness it in meaningful ways. One option to do this is via deep learning.

Deep learning is a smaller topic under the artificial intelligence (AI) umbrella. It’s a methodology that aims to build connections between data (lots of data!) and make predictions about it.

Here’s more on the concept of deep learning and how it can prove useful for businesses.

Table of Contents

Narrowing down information.

Definition: What Is Deep Learning?

Let’s start with a deep learning definition — what is it, exactly?

Deep learning (also called deep learning AI) is a form of machine learning that builds neural-like networks, similar to those found in a human brain. The neural networks make connections between data, a process that simulates how humans learn.

Neural nets include three or more layers of data to improve their learning and predictions. While AI can learn and make predictions from a single layer of data, additional layers provide more context to the data. This optimizes the process of making more complex and detailed connections, which can lead to greater accuracy.

We cover neural networks in a separate blog, which you can check out here.

Deep learning algorithms are the driving force behind many applications of artificial intelligence, including voice assistants, fraud detection, and even self-driving cars.

The lack of pre-trained data is what makes this type of machine learning so valuable. In order to automate tasks, analyze data, and make predictions without human intervention, deep learning algorithms need to be able to make connections without always knowing what they’re looking for.

comparing artificial intelligence to machine learning and deep learning.

What’s the Difference Between Machine Learning vs. Deep Learning?

Machine learning and deep learning share some characteristics. That’s not surprising — deep learning is one type of machine learning, so there’s bound to be some overlap.

But the two aren’t quite the same. So what's the difference between machine learning and deep learning?

When comparing machine learning vs. deep learning, machine learning focuses on structured data, while deep learning can better process unstructured data. Machine learning data is neatly structured and labeled. And if unstructured data is part of the mix, there’s usually some pre-processing that occurs so that machine learning algorithms can make sense of it.

With deep learning, data structure matters less. Deep learning skips a lot of the pre-processing required by machine learning. The algorithms can ingest and process unstructured data (such as images) and even remove some of the dependency on human data scientists.

For example, let’s say you have a collection of images of fruits. You want to categorize each image into specific fruit groups, such as apples, bananas, pineapples, etc. Deep learning algorithms can look for specific features (e.g., shape, the presence of a stem, color, etc.) that distinguish one type of fruit from another. What’s more, the algorithms can do so without first having a hierarchy of features determined by a human data expert.

As the algorithm learns, it can become better at identifying and predicting new photos of fruits — or whatever use case applies to you.

Types of Deep Learning vs. Machine Learning

Another differentiation between deep learning vs. machine learning is the types of learning each is capable of. In general terms, machine learning as a whole can take the form of supervised learning, unsupervised learning, and reinforcement learning.

  • Supervised learning requires labeled or structured datasets. This type of learning requires human intervention to ensure data is labeled appropriately.
  • Unsupervised machine learning doesn’t require datasets to be labeled. Instead, it focuses on pattern recognition in the data and will connect the dots based on shared characteristics. As you may have guessed, there’s no human involvement in feeding unlabeled data into an unsupervised machine learning algorithm.
  • Then we have deep reinforcement learning, where a model “learns” to become more accurate based on a feedback and reward system. This technique is considered semi-supervised.
    Reinforcement learning trains machine learning models to make a series of decisions while earning penalties or rewards based on the effectiveness of its decisions.

Deep learning applies mostly to unsupervised machine learning and deep reinforcement learning. By making sense of data and making complex decisions based on large amounts of data, companies can improve the outcomes of their models, even when some information is unknown.

Input processing and then output.

How Does Deep Learning Work?

In deep learning, a computer model learns to perform tasks by considering examples rather than being explicitly programmed. The term "deep" refers to the number of layers in the network — the more layers, the deeper the network.

Deep learning is based on artificial neural networks (ANNs). These are networks of simple nodes, or neurons, that are interconnected and can learn to recognize patterns of input. ANNs are similar to the brain in that they are composed of many interconnected processing nodes, or neurons. Each node is connected to several other nodes and has a weight that determines the strength of the connection.

Layer-wise, the first layer of a neural network extracts low-level features from the data, such as edges and shapes. The second layer combines these features into more complex patterns, and so on until the final layer (the output layer) produces the desired result. Each successive layer extracts more complex features from the previous one until the final output is produced.

This process is also known as forward propagation. Forward propagation can be used to calculate the outputs of deep neural networks for given inputs. It can also be used to train a neural network by back-propagating errors from known outputs.

Backpropagation is a supervised learning algorithm, which means it requires a dataset with known correct outputs. Backpropagation works by comparing the network's output with the correct output and then adjusting the weights in the network accordingly. This process repeats until the network converges on the correct output. Backpropagation is an important part of deep learning because it allows for complex models to be trained quickly and accurately.

This process of forward and backward propagation is repeated until the error is minimized and the network has learned the desired pattern.

Deep Learning Models

Let's look at some types of deep learning models and neural networks:

Convolutional Neural Networks (CNN)

Convolutional neural networks (or just convolutional networks) are commonly used to analyze visual content.

They are similar to regular neural networks, but they have an extra layer of processing that helps them to better identify patterns in images. This makes them particularly well suited to tasks such as image recognition and classification.

Recurrent Neural Networks (RNN)

A recurrent neural network (RNN) is a type of artificial neural network where connections between nodes form a directed graph along a sequence. This allows it to exhibit temporal dynamic behavior.

Unlike feedforward neural networks, RNNs can use their internal memory to process sequences of inputs. This makes them valuable for tasks such as unsegmented, connected handwriting recognition or speech recognition.

Long Short-Term Memory (LSTM)

Long short-term memory networks are a type of recurrent neural network that can learn and remember long-term dependencies. They are often used in applications such as natural language processing and time series prediction.

LSTM networks are well suited to these tasks because they can store information for long periods of time. They can also learn to recognize patterns in sequences of data.

Deep learning processing.

How Can You Apply Deep Learning to Your Business?

Wondering what challenges deep learning and AI can help you solve? Here are some practical examples where deep learning can prove invaluable.

Using Deep Learning for Sentiment Analysis

Sentiment analysis is the process of extracting and understanding opinions expressed in text. It uses natural language processing (another AI technology) to detect nuances in words. For example, it can distinguish whether a user’s comment was sarcastic, humorous, or happy. It can also determine the comment’s polarity (positive, negative, or neutral) as well as its intent (e.g., complaint, opinion, or feedback).

Companies use sentiment analysis to understand what customers think about a product or service and to identify areas for improvement. It compares sentiments individually and collectively to detect trends and patterns in the data. Items that occur frequently, such as lots of negative feedback about a particular item or service, can signal to a company that they need to make improvements.

Deep learning can improve the accuracy of sentiment analysis. With deep learning, businesses can better understand the emotions of their customers and make more informed decisions.

Improving Business Processes

Deep learning can enable businesses to automate and improve a variety of processes.

In general, businesses can use deep learning to automate repetitive tasks, speed up decision making, and optimize operations. For example, deep learning can automatically categorize customer support tickets, flag potentially fraudulent transactions, or recommend products to customers.

Deep learning can also be used to improve predictive modeling. By using historical data, deep learning can predict demand for a product or service and help businesses optimize inventory levels.

Additionally, deep learning can identify patterns in customer behavior in order to better target marketing efforts. For example, you might be able to find better marketing channels for your content based on user activity.

Overall, deep learning has the potential to greatly improve various business processes. It helps you answer questions you may not have thought to ask. By surfacing these hidden connections in your data, you can better approach your customers, improve your market positioning, and optimize your internal operations.

Optimizing Your Marketing Strategy

If there’s one thing marketers don’t need more of, it’s guesswork. Connecting with your target audience and catering to their specific needs can help you stand out in a sea of sameness. But to make these deeper connections, you need to know your target audience well and be able to time your outreach.

One way to use deep learning in sales and marketing is to segment your audience. Use customer data (such as demographic information, purchase history, and so on) to cluster customers into groups. From there, you can use this information to provide customized service to each group.

Another way to use deep learning for marketing and customer service is through predictive analysis. This involves using past data (such as purchase history, usage patterns, etc.) to predict when customers might need your services again. You can send targeted messages and offers to them at critical times to encourage them to do business with you.

How Meltwater Helps You Harness Deep Learning Capabilities

Advances in machine learning, like deep learning models, give businesses more ways to harness the power of data analytics. Taking advantage of purpose-built platforms like Meltwater gives you a shortcut to applying deep learning in your organization.

At Meltwater, we use state-of-the-art technology to give you more insight into your online presence. We’re a complete end-to-end solution that combines powerful technology and data science technique with human intelligence. We help you turn data into insights and actions so you can keep your business moving forward.

Contact us today for a free demo!

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