Deep Learning vs. Machine Learning: What's the Difference?

When it comes to analyzing data, identifying patterns, and predicting consumer behavior, companies have a powerful ally: artificial intelligence (AI). However, before capitalizing on AI to reach your customers, you'll need to understand the differences between deep learning vs. machine learning.

While machine learning and deep learning sound similar, they're not synonymous. As subsets of AI, these technologies help applications process, analyze, and learn from information without being explicitly programmed by a human. By understanding these technologies — and how they bring humans and AI agents together — you can serve customers better while helping employees be more productive. In this article, we'll explore machine learning vs. deep learning and help you understand the benefits of using both.

What we'll cover:

• What is machine learning?
• What is deep learning?
• How machine learning paves the way for AI development
• Advantages of deep learning
• Key differences between deep learning vs. machine learning
• When should you use deep learning vs. machine learning models?

Machine learning (ML) is a subset of AI that lets machines continually learn from new results and information, becoming more intelligent and capable over time. It involves the development of algorithms and models that enable computers to gain information from data, identify patterns, and make predictions or decisions without being explicitly programmed. Using this process, machines can improve their performance through experience and data analysis.

In machine learning, AI relies heavily on algorithms to learn from data. Some examples include large language models (LLMs), used in natural language processing (NLP), and small language models, used in a similar context on a smaller scale.

Within machine learning are two fundamental approaches: supervised learning and unsupervised learning.

Supervised learning involves training a model using labeled data, where the input data is paired with corresponding output labels. The goal is for the model to learn the mapping between the input and output variables, enabling it to make accurate predictions or classifications on unseen data. The model learns from the provided examples and is guided by accurate and known answers, allowing it to generalize and make predictions on new, unlabeled data.

Some common applications of supervised learning include analyzing customer data to identify helpful patterns. For example, a business could train its AI on labeled historical data to uncover accounts that are in danger of switching to a competitor.

On the other hand, unsupervised learning deals with unlabeled data, where the model aims to discover patterns, structures, or relationships within the data without any predefined output labels. The goal is to uncover hidden insights or groupings in the data, often through techniques like clustering or dimensionality reduction. Unsupervised learning allows the model to learn independently and identify inherent patterns or structures that may not be apparent to human observers.

One common use of unsupervised learning is seen in streaming services. When you open up a streaming service, chances are you'll see a category called "your next watch" or "you might like." Based on your viewing history, the platform's AI will identify common patterns to serve you additional content that aligns with your interests.

Deep learning is a subset of machine learning inspired by how the human brain works. It does this by training multilayer artificial neural networks (ANNs) to learn from and extract hierarchical representations of data. It enables the model to automatically learn complex patterns and features from raw data, leading to highly accurate predictions and decision-making.

Before we discuss deep learning vs. machine learning, let's dive into the role neural networks play in deep learning and its key strength, hierarchical learning.

Neural networks are inspired by the structure and functioning of the human brain. They consist of interconnected nodes, called neurons, organized in layers. Each neuron receives input signals, performs computations, and produces an output signal.

The architecture of a neural network typically consists of three types of layers: an input layer, hidden layers, and an output layer.

• The input layer receives the raw data, such as images or text, and passes it to the hidden layers.
• The hidden layers then perform computations on the input data by applying weights and biases to the inputs.
• The output layer produces the final predictions or classifications based on the computations performed in the hidden layers. Each neuron in the output layer represents a specific class or prediction.

During training, the network adjusts the weights and biases in the hidden layers to minimize the difference between the predicted output and the actual output.

The strength of neural networks is their ability to understand hierarchical representations of data. Each layer in the network learns to extract and represent different levels of abstraction.

The initial layers capture low-level features like edges or textures, while deeper layers learn more complex and abstract features. This hierarchical representation allows neural networks to understand and recognize patterns in data, making them powerful tools for tasks like image recognition, NLP, and speech recognition.

Machine learning is critical for driving AI development. Because machine learning takes data and learns from it, it can recognize patterns, change its behavior, and improve over time. Also, machine learning models don't require human programmers to continually feed them directions, so they can spur rapid advancements in AI's power and functionality and help AI agents work autonomously.

In rules-based programming, humans supply a computer with if/then statements that lay out how it should react when a given event occurs. A classic example of this technology is a traditional chatbot. But in data-driven models, the computer itself can recognize patterns and adapt to complex situations and information. It's important to note that machine learning's ability to learn and improve over time is key to its effectiveness.

Machine learning has wide functionality and provides benefits across various industries. Let's look at a few common applications:

• Speech recognition: In everyday life, you use this function anytime you give a smart device a voice command or speak a choice aloud from an automated customer service menu. In business, speech recognition has become a key tool for capturing and summarizing conversations. For example, during a sales meeting with a prospect, this tech can analyze the call recording, identify the key points, and create a text file of notes for the sales rep to review later.
• Image recognition: This ML feature makes it possible for computers to put images into context and index them as information. It’s what helps you unlock a smartphone with facial identification or have an app find possible product pages based on a picture of an item. Practical business uses include field service technicians uploading images to identify service repairs needed in the field or retailers snapping pictures of stocked shelves for automated inventory management.
• Predictive analytics: Analysis driven by machine learning has unlimited applications for businesses. Some of the most common uses are helping with sales forecasting, targeting customers with relevant marketing, or fraud detection for financial services.
• Personalized recommendations: Intelligent product recommendations — such as those supplied by an autonomous agent on an ecommerce site — are powered by machine learning.
• Business optimization: machine learning can also help AI applications identify waste and improve resource allocation in business operations. In manufacturing, for example, taking AI’s recommendations for time savings or process improvement can help a factory boost productivity.

There are many advantages to using AI, including deep learning, which can understand and extract information from your unstructured customer data, such as images, text, and audio, without the need for labeling or categorization.

By using neural networks with multiple layers, deep learning models can automatically detect complex patterns and representations from raw data. That lets deep learning algorithms uncover otherwise hidden insights and make accurate predictions, unlocking applications in fields like computer vision, natural language processing, and speech recognition.

Let’s look at the computer vision field first.

• Detection and classification of objects: Deep learning algorithms can analyze real-time video feeds to detect and classify objects, enabling safer and more efficient self-driving cars.
• Real-time monitoring and identification: Deep learning also allows for real-time monitoring and identification of potential threats, enhancing security measures for surveillance systems.

In NLP, deep learning has transformed language-related tasks. Deep learning models, such as recurrent neural networks (RNNs) and transformers, have revolutionized language translation, sentiment analysis, and AI assistants. This has significant business implications, including:

• Deep learning-powered chatbots: Chatbots that use conversational AI can provide personalized and efficient customer support, reducing costs and improving customer satisfaction.
• Sentiment analysis: Powered by deep learning, sentiment analysis allows businesses to understand customer feedback and sentiment at scale, enabling them to make data-driven decisions and improve their products and services.

The combination of computer vision and NLP has opened up new possibilities. For example, deep learning models can analyze images and extract textual information, enabling applications like automatic image captioning and content moderation. This has implications for industries such as social media, ecommerce, and content creation.

Deep learning also holds promise for emerging applications such as using generative AI for content creation. It’s a powerful possibility for service teams who need to populate knowledge management repositories for both customers and customer support representatives.

Deep learning can also help sales organizations by making lead management and qualification easier (and more accurate) as well as assisting with upselling opportunities based on a customer’s history and preferences.

Deep learning is a type of machine learning, but it differs in its approach and capabilities in the following ways:

• Machine learning: This technology focuses on algorithms that learn from data and make predictions or decisions without being explicitly programmed. It relies on feature engineering, where human experts extract relevant features from data to train the models. Machine learning algorithms typically work well with structured data and perform effectively in tasks like regression, classification, and clustering.
• Deep learning: These models are composed of multiple layers of interconnected nodes (neurons) that learn representations of data at different levels of abstraction. Unlike machine learning, deep learning algorithms can automatically learn features from raw data, eliminating the need for manual feature engineering. This makes deep learning particularly effective in handling unstructured data, such as images, audio, and text.

Here are some other ways these technologies differ.

Machine learning often requires manual feature engineering, where domain experts extract relevant features from data. These features serve as inputs for machine learning algorithms. For example, in a spam email classification task, an ML model might rely on features like the presence of certain keywords or the length of the email to make a decision.

Deep learning models can automatically learn from raw data. This eliminates the need for explicit feature engineering. For instance, in image classification, a deep learning model can learn to recognize edges, shapes, and textures directly from the pixel values of the images. This allows virtual agents to handle unstructured data, such as images, audio, and text, without manual intervention.

Machine learning models typically require a moderate amount of labeled data to perform well. The size and quality of the training data play a crucial role in the accuracy of the model. Machine learning models can often be trained on smaller datasets and can scale well with limited computational resources.

Deep learning models generally require a large amount of labeled data to achieve optimal performance. This is achieved by using LLMs, which can be used to create LLM agents. The more data available for training, the better the deep learning model can grasp complex patterns and generalize unseen examples. Deep learning models also benefit from powerful computational resources, such as GPUs, to handle the large-scale computations involved in training deep neural networks.

Machine learning models often provide more transparency. Since the features are manually engineered, it’s easier to understand and interpret the relationship between the input features and the model's predictions. For example, in a linear regression model, the coefficients assigned to each feature can provide insights into their importance and impact on the output.

Alternatively, deep learning models are often considered "black boxes" due to their complex architectures and the automatic feature learning process. While they can achieve high accuracy, understanding the internal workings and explaining the decision-making process of deep learning models can be challenging. However, efforts are being made to develop techniques for interpreting and explaining deep learning models, such as feature visualization and attention mechanisms.

Deep learning and machine learning models offer unique value, with strengths and weaknesses. For example, if you’re trying to solve complex, intricate problems, deep learning might be the right choice. However, that comes with a need for more computing power and larger datasets.

Let's look closer at when to use deep learning vs. machine learning models, starting with deep learning:

• Image classification and speech recognition: Deep learning can enable medical applications to identify and classify different types of illnesses by comparing x-rays, as well as help digital assistants recognize and act on voiced queries.
• Language translation: When you use the translate feature on a web page or a dedicated language app, deep learning is at work.
• Sentiment analysis: Deep learning is essential for this task, which is important for both sales and service teams. AI can help these teams understand customer sentiment, providing better context to customer data.
• Chatbot development: Customers expect modern chatbots that can do much more than older models. For instance, deep learning enables chatbots to offer on-target production suggestions on ecommerce sites.
• Realistic content: Deep learning makes it possible to generate lifelike images, videos, and music. However, those applications require ‌large datasets inherent to deep learning.

Because machine learning models rely on structured data, they excel at certain applications. Here are some:

• Consumer credit checks: Machine learning models can help financial services applications conduct credit checks quickly and accurately.
• Financial fraud detection: Beyond credit checks, financial services businesses can build AI applications using machine learning to mitigate fraud.
• Maintenance needs for machinery: Machine learning is perfect for identifying when parts should be inspected, repaired, and replaced, eliminating guesswork and reducing the chance of machinery failure.
• Customer behavior predictions: Using datasets based on customer data, AI applications powered by machine learning can help sales teams identify customers who are likely to leave for a competitor.

Deep learning and machine learning applications together serve as the backbone of artificial intelligence, driving innovation and reshaping how we interact with technology.

Understanding deep learning vs. machine learning — and the advantages and real-world applications of these technologies — puts businesses in a good position to develop a strong AI strategy and achieve a positive ROI.

Agentforce is a good place to start if you're ready to put AI agents, powered by deep learning and machine learning, to the test.