Machine learning (ML) is a subset of artificial intelligence (AI). It’s a set of approaches for implementing AI. ML is intended to give computer systems the ability to “learn”, using the provided data and make accurate predictions. As it can be guessed, deep learning (DL) is a subset of ML. It’s merely one of the ways for implementing ML. In other words, DL is the next evolution of machine learning.
DL algorithms can be considered as methods to identify patterns and classify various types of information, just like we use our brains to it. The brain usually is processing the information it receives through labeling and assigning the items into various categories. The concept of DL algorithms is similar to our brain’s absorbing new information: trying to compare it to a known thing before making sense of it, finding similarities. The most powerful algorithms, imitating the way our brains make decisions, are artificial neural networks (ANNs), and it’s not surprising that DL is based on them.
ANN is an information processing concept which is used to simulate the complex system behavior. The main idea of the ANN is to use the way as a human brain do to solve any specific problem. ANN is constructed by multiple nodes which model the biological neurons of a human brain. Nodes are organized in layers and connected for communication, usually between nearby layer. The data is passed through layers from the input layer to the output layer. The number of nodes in layers can be different.
DL is an ANN, but it has a series of layers, and each layer creates more abstract representations transforming the input data. This hierarchy of layers identifies the input features and creates a set of new features based on the data. The output layer combines all these features and makes a prediction. A simple ANN has only one hidden layer and cannot learn complex features like DL with its multiple hidden layers. The more layers it has, the higher the level of features are learned. The data and functions are exponentially related. Having ten features, for instance, requires to provide at least 100 data values. DL is expensive and requires massive datasets to train and tremendous computational resources. It was first theorized in the 1980s, and there are two main reasons it has only recently become useful:
DL requires large amounts of labeled data, and only recently appropriate data storages have been invented, and data labeling process bores fruit.
DL requires significant computing power. High-performance GPUs have a parallel architecture that is widely used for DL. In combination with clusters or cloud computing, it provides an opportunity to reduce training time.
DL is not only more hidden layers, but there are also several deep learning techniques, like convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs). DL has specialization in models like the brain has. It’d be a mistake to identify one network as better than the others; they are better suited to specific tasks. CNNs work well by extracting features from images. They’re realized while the system trains on a set of images, making CNN extremely accurate for computer vision tasks. CNNs exclude the manual feature extraction, and it works by extracting features directly from images.
While CNNs are typically used for image processing, the other class of DL models, RNNs, is used generally for language processing. They have built-in feedback loops, modeling the memory, where the output from layer might be fed back into itself, like in recursion, which gives a memory effect. RNNs can be trained for sequence generation by processing real data sequences for predicting what comes next.
In GANs, two neural networks compete playing different roles: generator and discriminator. The first one tries to create persuasive “fake” data while the other tries to find the difference between the fake and real data. Each training epoch makes the generator better in creating counterfeit data and the discriminator sharper at spotting the fakes. Both are improved in a confrontation like both sides have profit in real debates between people. GANs can be used for extremely interesting applications, including generating images from written text.
DL models have been applied to many fields and concepts like social network filtering, image recognition, financial fraud detection, speech recognition, computer vision, medical image processing, natural language processing, visual art processing, drug discovery and design, toxicology, bioinformatics, customer relationship management, audio recognition and so on. They proved the right to be in the top of the ML techniques; the depth of the DL is not in the number of layers but in the modeling of different specialized structures of our brain.