Machine learning and deep learning are techniques often mentioned in the same breath, but there are some important differences between the two. In many cases, organizations use both deep learning and machine learning together to become more efficient, productive, and innovative. However, it’s still important to understand the differences between these technologies so you can know when to use each one.
What is machine learning?
Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on systems that can learn and change when exposed to new data without being explicitly programmed. Machine learning makes it possible for machines to find patterns in large data sets and then use those patterns to make predictions about new data. It is based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.
How does machine learning work?
ML algorithms build models based on structured data. These models can then be used to make predictions about new data. The model’s predictions will become more accurate as it encounters more data.
Unlike traditional programming, which requires you to program instructions for your computer manually, machine learning allows software programs to analyze data, recognize patterns independently, and make decisions based on what they’ve learned. Machine learning applications are quickly becoming ubiquitous in our daily lives, from analyzing behaviors of potential customers to detecting fraud in financial transactions.
ML works by first training the system with lots of sample data known as training data. Then, the ML algorithm builds a predictive model using those samples. The prediction accuracy depends on the quality of the training data used to create the model.
What is deep learning?
Deep learning is a subset of machine learning, a field of computer science dedicated to giving computers advanced cognitive capabilities. In deep learning, an artificial neural network—essentially, software meant to mimic human learning—learns from large data sets and attempts to make connections between various inputs and outputs (or features).
How does deep learning work?
Deep learning is a part of a broader family of machine learning methods based on artificial neural networks (ANNs), with representations loosely inspired by biological neural networks. Representations are composed of multiple layers, and connections between layers form tensors. The deep learning algorithms can be used for supervised and unsupervised deep learning. The most common type of deep learning is related to artificial neural networks that use recurrent or convolutional neural network architecture.
A convolutional layer in a deep neural network has an input layer, one or more hidden layers, and an output layer. Each convolutional layer applies the same filter across all pixels in its input. It excels at spatial data like images.
Meanwhile, recurrent neural network architecture usually has an input layer and a single output layer. Recurrent networks process their inputs sequentially, repeating several times over time. They are commonly employed in natural language processing tasks requiring long-term dependencies such as the order of words in sentences.
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Differences between machine learning and deep learning
Machine learning deals with constructing and studying algorithms that can learn from data. On the other hand, deep learning is concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
The table below highlights some high-level comparisons between machine learning and deep learning:
|Machine learning||Deep learning|
|Machine learning is a subset of artificial intelligence.||Deep learning is a subset of machine learning.|
|ML deals with the creation of algorithms that can learn from and make predictions on data.||DL uses algorithms called neural networks to learn from data in a way that mimics the workings of the human brain.|
|A system performing machine learning typically involves three steps: training, testing, and evaluation.||A system performing deep learning typically involves four steps: training, validation (optional), testing, and evaluation.|
|ML is best for problems that are understood to some extent and have quality training data.||DL is best for more complex problems that may not be understood fully.|
|ML provides analysts with analytical models that can generate insights based on past outcomes. These models identify trends, relationships, and patterns in historical data. They also make predictions about future events by analyzing current data streams.||DL relies on parallelization—that is, breaking down a task so that it can be completed faster—which makes the technology better suited for extremely large datasets.|
|ML method requires less time to train the model, however testing the model takes a long time.||DL requires an extended processing time to train the model, but a shorter execution time to test the model.|
|ML has various applications including website optimization, improving customer experience, increasing customer loyalty, fraud detection, and credit card risk management.||DL applications include natural language processing, self-driving cars, medical imaging and diagnostics, recommendation engines, and image recognition.|
|ML only requires knowledge of how one algorithm operates.||DL systems are more complex than those of machine learning because they require an understanding of how each layer works together to form a complete picture to process information|
When to use deep learning vs machine learning
Deep learning is best for data that is unstructured or has a complex structure, such as images or text. It can also be used for time-series data. Machine learning is best for structured data that can be easily labeled, such as tabular data. If you are unsure which technique to use, consider these four questions:
- What type of data do I have?
- What kind of problem am I trying to solve?
- What is the volume of my dataset?
- Do I have a limited budget?
For example, if your dataset contains unstructured data like rich text, it would likely benefit from using deep learning techniques. On the other hand, if your dataset contains structured data like stock prices, it would be more appropriate to use machine learning techniques because this data can easily be labeled by categories.
With deep learning, users do not need to pre-define the type of algorithm to use because the system will automatically learn what pattern works best for the given dataset. Thus, this would be your best option if you are dealing with vast amounts of data and looking for accuracy.
In contrast, machine learning may be better if you are working with smaller, structured datasets. Because they require less computing power than deep learning, ML models are generally more versatile and easier to implement.
However, it’s important to note that even though both techniques have pros and cons, they’re typically complementary rather than mutually exclusive. Many use cases—including recommendation engines, website optimization, and fraud detection—can leverage both machine learning and deep learning to create robust tools.