What is deep learning?
Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning models can be taught to perform classification tasks and recognize patterns in photos, text, audio and other various data. It is also used to automate tasks that would normally need human intelligence, such as describing images or transcribing audio files.

Deep learning is an important element of data science, including statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier.

Where human brains have millions of interconnected neurons that work together to learn information, deep learning features neural networks constructed from multiple layers of software nodes that work together. Deep learning models are trained using a large set of labeled data and neural network architectures.

Deep learning enables a computer to learn by example. To understand deep learning, imagine a toddler whose first word is dog. The toddler learns what a dog is -- and is not -- by pointing to objects and saying the word dog. The parent says, "Yes, that is a dog," or, "No, that is not a dog." As the toddler continues to point to objects, he becomes more aware of the features that all dogs possess. What the toddler is doing, without knowing it, is clarifying a complex abstraction: the concept of dog. They are doing this by building a hierarchy in which each level of abstraction is created with knowledge that was gained from the preceding layer of the hierarchy.

Why is deep learning important?
Deep learning requires both a large amount of labeled data and computing power. If an organization can accommodate for both needs, deep learning can be used in areas such as digital assistants, fraud detection and facial recognition. Deep learning also has a high recognition accuracy, which is crucial for other potential applications where safety is a major factor, such as in autonomous cars or medical devices.

How deep learning works
Computer programs that use deep learning go through much the same process as a toddler learning to identify a dog, for example.

Deep learning programs have multiple layers of interconnected nodes, with each layer building upon the last to refine and optimize predictions and classifications. Deep learning performs nonlinear transformations to its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep.

In traditional machine learning, the learning process is supervised, and the programmer must be extremely specific when telling the computer what types of things it should be looking for to decide if an image contains a dog or does not contain a dog. This is a laborious process called feature extraction, and the computer's success rate depends entirely upon the programmer's ability to accurately define a feature set for dog. The advantage of deep learning is the program builds the feature set by itself without supervision.

Initially, the computer program might be provided with training data -- a set of images for which a human has labeled each image dog or not dog with metatags. The program uses the information it receives from the training data to create a feature set for dog and build a predictive model. In this case, the model the computer first creates might predict that anything in an image that has four legs and a tail should be labeled dog. Of course, the program is not aware of the labels four legs or tail. It simply looks for patterns of pixels in the digital data. With each iteration, the predictive model becomes more complex and more accurate.

Unlike the toddler, who takes weeks or even months to understand the concept of dog, a computer program that uses deep learning algorithms can be shown a training set and sort through millions of images, accurately identifying which images have dogs in them, within a few minutes.

To achieve an acceptable level of accuracy, deep learning programs require access to immense amounts of training data and processing power, neither of which were easily available to programmers until the era of big data and cloud computing. Because deep learning programming can create complex statistical models directly from its own iterative output, it is able to create accurate predictive models from large quantities of unlabeled, unstructured data.

Deep learning methods
Various methods can be used to create strong deep learning models. These techniques include learning rate decay, transfer learning, training from scratch and dropout.

Learning rate decay
The learning rate is a hyperparameter -- a factor that defines the system or sets conditions for its operation prior to the learning process -- that controls how much change the model experiences in response to the estimated error every time the model weights are altered. Learning rates that are too high may result in unstable training processes or the learning of a suboptimal set of weights. Learning rates that are too small may produce a lengthy training process that has the potential to get stuck.

The learning rate decay method -- also called learning rate annealing or adaptive learning rate -- is the process of adapting the learning rate to increase performance and reduce training time. The easiest and most common adaptations of learning rate during training include techniques to reduce the learning rate over time.

Transfer learning
This process involves perfecting a previously trained model; it requires an interface to the internals of a preexisting network. First, users feed the existing network new data containing previously unknown classifications. Once adjustments are made to the network, new tasks can be performed with more specific categorizing abilities. This method has the advantage of requiring much less data than others, thus reducing computation time to minutes or hours.

Training from scratch
This method requires a developer to collect a large, labeled data set and configure a network architecture that can learn the features and model. This technique is especially useful for new applications, as well as applications with many output categories. However, overall, it is a less common approach, as it requires inordinate amounts of data, causing training to take days or weeks.

Dropout
This method attempts to solve the problem of overfitting in networks with large amounts of parameters by randomly dropping units and their connections from the neural network during training. It has been proven that the dropout method can improve the performance of neural networks on supervised learning tasks in areas such as speech recognition, document classification and computational biology.

Deep learning neural networks
A type of advanced machine learning algorithm, known as an artificial neural network (ANN), underpins most deep learning models. As a result, deep learning may sometimes be referred to as deep neural learning or deep neural network (DDN).

DDNs consist of input, hidden and output layers. Input nodes act as a layer to place input data. The number of output layers and nodes required change per output. For example, yes or no outputs only need two nodes, while outputs with more data require more nodes. The hidden layers are multiple layers that process and pass data to other layers in the neural network.

Neural networks come in several different forms, including the following:

Recurrent neural networks.
Convolutional neural networks.
ANNs and feed.
Forward neural networks.
Each type of neural network has benefits for specific use cases. However, they all function in somewhat similar ways -- by feeding data in and letting the model figure out for itself whether it has made the right interpretation or decision about a given data element.

Neural networks involve a trial-and-error process, so they need massive amounts of data on which to train. It's no coincidence neural networks became popular only after most enterprises embraced big data analytics and accumulated large stores of data. Because the model's first few iterations involve somewhat educated guesses on the contents of an image or parts of speech, the data used during the training stage must be labeled so the model can see if its guess was accurate. This means unstructured data is less helpful. Unstructured data can only be analyzed by a deep learning model once it has been trained and reaches an acceptable level of accuracy, but deep learning models can't train on unstructured data.

Deep learning benefits
Benefits of deep learning include the following:
Automatic feature learning. Deep learning systems can perform feature extraction automatically, meaning they don't require supervision to add new features.
Pattern discovery. Deep learning systems can analyze large amounts of data and uncover complex patterns in images, text and audio and can derive insights that it might not have been trained on.
Processing of volatile data sets. Deep learning systems can categorize and sort data sets that have large variations in them, such as in transaction and fraud systems.
Data types. Deep learning systems can process both structured and unstructured data.
Accuracy. Any additional node layers used aid in optimizing deep learning models for accuracy.
Can do more than other machine learning methods. When compared to typical machine learning processes, deep learning needs less human intervention and can analyze data that other machine learning processes can't do as well.