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Deep learning is a type of machine learning, which is a subset of artificial intelligence.
Deep Learning vs Machine Learning – What’s the Difference?
AI can be boiled down to two concepts: machine learning and deep learning. These are put into use everywhere in our lives whether it be making self-driving cars, how Netflix knows what shows you might want to watch next, or how photos get identified. Even though "machine learning" and "deep learning" seem to be used interchangeably, there are major differences between the two.
In a general sense, machine learning is a term for when computers learn from data. Data is fed into an algorithm and is used to train, test, and deploy a model. The model is then used to do an automated task and will continue to work based on what it has previously learned. Because of this, the algorithms’ performance improves as they are exposed to more data over time. It is commonly used in speech recognition, email spam detectors, stock markets, and to predict weather.
This is the most common form of learning and requires the most ongoing human participation. It focuses on giving data to learning algorithms to provide context and feedback, called "training data". Once the model is in place, the data scientist can confirm accurate prediction or issue corrections but it is not efficient to keep monitoring the computer’s performance and making adjustments.
With unsupervised learning, data sets only include the inputs and the machine learning algorithms must find patterns and commonalities between data points in order to determine the next steps to take. A common use for unsupervised learning is ‘clustering‘, where the computer organizes the data into common themes and layers.
Semi-supervised learning sits between supervised and unsupervised learning. It involves a small number of labeled examples and a large number of unlabeled examples. The model must learn and make predictions on the new examples making the following assumptions:
- Continuity Assumption
- Cluster Assumption
- Manifold Assumption
Reinforcement learning emphasizes learning agents that learns through cumulative reward, based on different actions. The computer would figure out how to get specific tasks done through trial-and-error. This type of learning is critical to help machines master complex tasks that have large, highly flexible, and unpredictable datasets.
Deep learning neural networks, also known as artificial neural networks, try to simulate the human brain using a mixture of data inputs, weights, and biases. These aspects collaborate to detect, categorize, and characterize things in data effectively.
How Deep Learning Works?
A deep learning model is designed to analyze data similarly to how a human brain would draw conclusions. Deep learning applications uses a layered structure of algorithms called an artificial neural network (ANN), which is inspired by the biological network of neurons in the human brain, leading to a process of learning that is far more capable than standard machine learning models.
Deep learning is rapidly transforming many industries, including healthcare, energy, finance, and transportation, making them rethink traditional business processes. It approaches complex tasks by breaking them down into "layers". The breaking down of tasks into smaller sub-tasks that collectively emerge into a larger system of learning is only limited to the size and depth of the neural network.
These networks are specially built algorithms designed to work with images. The ‘convolution’ in the title is the process that applies a weight-based filter across ever element of an image, helping the computer understand elements within the image. This is helpful when a high volume of images needs to be scanned for a specific item or feature.
On the other hand, these networks introducce a key element into machine learning – memory. The computer is able to keep past data point and decisions to consider them when reviewing current data. This introduces context into the picture. Recurrent neural networks is a major focus point for natural language processing work. For example, driving directions are more accurate if the computer has the memory of everyone following a recommended route on Tuesday afternoon takes twice as long to get where they are going.
Machine learning requires more ongoing human intervention to get results.
Deep learning systems take more time to set up but can generate results instantaneously. Note, the quality of the performance is likely to improve over time as more data becomes available.
Deep learning requires much more data than a traditional machine learning algorithm to function properly. Machine learning works with a thousand data points but deep learning oftentimes works with millions.
Machine learning tends to require structured data and traditional algorithms like linear regression while deep learning employs neural networks and is built to accommodate large volumes of unstructured data.
Machine learning can work on low-end machine and doesn’t need a large amount of computational power. Deep learning depends on high-end machines since it does a large number of matrix multiplication operations.
Machine learning allows to comparably quickly train a machine learning model based on data. Deep learning requires intensive computation to train neural networks with multiple layers.
Compared to machine learning, deep learning’s self-training capabilities enable faster and more accurate results.
Deep learning technology enables more complex and autonomous programs such as self-driving cars or robots that perform advanced surgeries.
The output for machine learning algorithms is usually a numerical value, like a score or a classification. For deep learning, the output can have multiple formats, such as a text, a score or a sound.
Data labeling may become costly and time-consuming. With deep learning, well-labeled data is unnecessary since algorithms excel without guidelines.
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