What Is Deep Learning: Definition, Framework, and Neural Networks
Deep learning is an emerging field of AI and ML and is currently in the focus of AI researchers and practitioners worldwide.
Deep learning is an emerging field of artificial intelligence (AI) and machine learning (ML) and is currently in the focus of AI researchers and practitioners worldwide. Deep learning is classified under machine learning, and its ability to learn without human supervision is what sets it apart. In this article, we will learn what deep learning and neural networks are, along with the frameworks used to create them. We’ll also look at some examples of neural network algorithms. Let’s delve deeper.
Table of Contents
What Are Neural Networks?
Types of Neural Networks
What Is Deep Learning?
Deep learning is a sub-discipline within machine learning, which itself is a subset of artificial intelligence. The primary distinguishing factor between machine learning and deep learning is that the latter is more complex. In deep learning, this complexity is described in the relationship that variables share. There are many fields of variables in deep learning problems, leading to more complicated relationships between them.
An expert system that utilizes simple concepts and relationships between variables to learn more complex concepts is known as a deep learning algorithm. The reason deep learning is called so is because the concepts are cumulative – building up from simple to complex. When plotted graphically, the process has many layers, leading to the term ‘deep learning’.
Deep learning algorithms differ from machine learning algorithms in their ability to learn from unstructured and unlabeled data. Usually, human help is required in machine learning to label the data and make it readable by the program, with ML algorithms dealing with structured, labeled data. However, deep learning algorithms can process this data accurately without the need for human labeling.
Deep learning algorithms can simply learn the associations between various points in a data set. Instead of requiring a human to sift through the data and label each data point with a set of attributes, the algorithm can assign these attributes to each data point on its own.
Neural networks and deep learning (DL) have usurped AI and ML in terms of being used as a buzzword. These technologies represent the natural way forward for artificial intelligence as we know it, paving the way for a human-like AI in the future.
Even though deep learning appeared on the radar of artificial intelligence practitioners and researchers in the late 90s, its adoption has been held back by the lack of computing power. Modern machine learning algorithms were conceived later and implemented more quickly, while deep learning has recently come to the forefront with the increasing amount of computing power in the world. However, more and more companies are now starting to see the potential for deep learning solutions.
What Are Neural Networks?
Neural networks are currently the de facto way to deploy deep learning solutions. This concept of deep learning closely mimics the way the human brain works by creating an artificial network of neurons. This imitation is more commonly known as an artificial neural network (ANN) and consists of many layers of neurons to accurately process a large amount of information.
Artificial neural networks are a type of deep learning algorithm that mimic the physical structure of the human brain. Our brain is made up of neurons, which work together to form our brain. The average adult’s brain consists of 100 billion neurons, all working in a unified fashion to control the human body and mind.
Artificial neural networks emulate this structure of the human brain in the way they are created, thus substituting organic neurons for mathematical functions. Just like the brain, there are many neurons in an artificial neural network, each with a specific function that classifies the data given to the program. These artificial neurons are structured in layers, with the data passing through each layer for interpretation by the machine.
An artificial neural network usually comprises many layers of neurons, also known as nodes. These nodes are programmed with ‘weights’, which act as a filter for the data passing through the nodes. Usually, these weights dictate how the data should be processed by the nodes in the network, and each node either amplifies or suppresses the importance of input.
The accurate weighing of the neural networks results in an accurate result, as the nodes will eventually learn which data is worth giving importance to and which isn’t. As time passes, suppressed inputs will not make it through the first few layers of the network, as each layer filters a unique field in the data.
The first few layers of nodes in a neural network are known as the input layer, with the last few ones being termed the output layer. In between these two layers, there is a hidden layer. This layer is called so because the input or output of this layer cannot be seen. This hidden layer is an integral part of neural networks and is also the reason that the technology cannot be easily explained.
Neural networks excel at finding patterns in data, recognizing the relationship between variables, clustering, and classification. They are generally deployed in enterprise scenarios where such tasks are required. Different types of neural networks have different applications. Let’s take a look at some of the neural networks being used today.
Types of Neural Networks
Neural networks are classified depending on their architecture and how the nodes are placed. They range from simple neural networks with only one to three layers to more complex ones having more than ten layers. Let’s look at some artificial neural networks.
1. Multilayer Perceptron
The simplest kind of neural network is known as a perceptron, which usually takes inputs from two or more input nodes directly into an output node. There is no hidden layer involved in this type of neural network.
Simply put, a multilayer perceptron is a neural network that resembles a perceptron, except with a higher number of layers. Multilayer perceptrons were among the first neural networks to be developed due to its simple nature. These networks utilize a technique known as backpropagation to improve themselves with every iteration. Backpropagation is a supervised learning method that sheds light on the gradient of the errors in the neural network.
Such networks are simple in their functioning and usually have a nonlinear function on each node. They are commonly used for complex problems that require approximate solutions and are utilized in speech recognition and machine translation. However, they have fallen out of use in favor of more powerful networks.
2. Convolutional Neural Networks
Convolutional neural networks (CNN) are a type of neural network that can assign importance to the given data on its own, taking the load off human labor to accurately label and classify the data. Usually, data pre-processing takes up a lot of the workflow in implementing an AI solution. CNNs can drastically reduce the amount of time it takes to pre-process data, as it can label the data on its own.
Convolutional neural networks are based on the structure of the animal brain, more specifically the visual cortex. The pattern of connection between the nodes in the network is similar to the way the visual cortex’s neurons. They are known as ‘convolutional’ networks as they employ a mathematical operation known as convolution.
While using a CNN, the data can be utilized while implementing minimal pre-processing methods, as the network itself can label and process the data accurately. In a nutshell, convolutional neural networks can reduce the data in an easily processable manner without losing the crux of the data. These neural networks are utilized in video and image recognition, along with natural language processing and recommendation engines.
3. Recurrent Neural Networks
Recurrent neural networks (RNN) utilize many inputs, outputs, and hidden layers in series, with the network being able to memorize past inputs and outputs. These networks make future decisions based not only on the data given to them at that point in time but also based on the past data that they had ingested. This makes recurrent neural networks a great fit for unsegmented workloads, for example, speech recognition.
Recurrent neural networks stand apart from other neural networks as the data does not move through the network in one direction. They can ‘recur’ the data and bring it back through its nodes while still maintaining the knowledge they gained from the first iteration. This is especially useful in many of the circumstances where recurrent neural networks are used, as they can utilize the knowledge of both past and present data to perceive future data in a suitable manner.
A specific subcategory of RNNs, known as long short-term memory (LSTM) networks, are taking the enterprise world by storm. Long short-term memory networks are comprised of input and output nodes like other neural networks and make an addition in the form of a forget gate. This forget gate sets apart LSTMs over other RNNs, as the flow of information to nodes can be easily controlled.
Deep Learning Frameworks
As with any other AI algorithm, a programming framework is required to create deep learning algorithms. These are usually extensions of existing frameworks, or specialized frameworks developed to create deep learning algorithms. Each framework comes with its own drawbacks and advantages. Let’s delve deeper into some of the most popular and powerful deep learning frameworks.
1. TensorFlow
Flow is a machine learning and deep learning framework that was created and released by Google in 2015. TensorFlow is the most popular deep learning framework in use today, as it is not only used by big leaders like Google, NVIDIA, and Uber, but also by data scientists and AI practitioners on a daily basis.
TensorFlow is a library for Python, although work is being done to port it to other popular languages like Java, JavaScript, C++, and more. However, a lot of resources are required to create a deep learning model with TensorFlow, as it relies on using a lot of coding to specify the structure of the network.
A commonly cited drawback of TensorFlow is that it operates with a static computation graph meaning that the algorithm has to be run every time to see the changes. However, the platform itself is extremely extensible and powerful, contributing to its high degree of adoption.
2. PyTorch
In many ways, PyTorch is TensorFlow’s primary competitor in the deep learning framework market. Developed and created by Facebook, PyTorch is an open-source deep learning framework that works with Python. Apart from powering most of Facebook’s services, other companies, such as Johnson & Johnson, Twitter, and Salesforce.
As suggested by its name, PyTorch offers in-built support for Python, and even allows users to use the standard debuggers provided by the software. As opposed to TensorFlow’s static graph, PyTorch has a dynamic computation graph, allowing users to easily see what effect their changes will have on the end result while programming the solution.
The framework offers easier options for training neural networks, utilizing modern technologies like data parallelism and distributed learning. The community for PyTorch is also highly active, with pre-trained models being published on a regular basis. However, TensorFlow beats PyTorch in terms of providing cross-platform solutions, as Google’s vertical integration with Android offers more power to TF users.
3. Keras
Keras is a deep learning framework that is built on top of other prominent frameworks like TensorFlow, Theano, and the Microsoft Cognitive Toolkit (CNTK). Even though it loses out to PyTorch and TensorFlow in terms of programmability, it is the ideal starting point for beginners to learn neural network.
Keras allows users to create large and complex models with simple commands. While this means that it is not as configurable as its competitors, creating prototypes and proofs-of-concept is a much easier task. It is also accessible as an application programming interface (API), making the software accessible in any scenario.
Closing Thoughts for Techies
Deep learning and neural networks are at the forefront of AI research and technology today. This sets the stage for even more advancements in deep learning, as research progresses, and newer methods enter the mainstream.
With the rise of open-source and accessible tools like TensorFlow and Keras, deep learning is sure to get the ball rolling in terms of enterprise adoption. Supporting infrastructure, such as powerful and accessible cloud computing and marketplaces for pre-trained models, are also laying the groundwork for greater adoption of the technology.
Working professionals in the AI space must learn skills that can be used in deep learning applications. Neural networks may hold the key to a future where AI can function at levels that are unheard of today.
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