Except for artificial intelligence buffs, most people have probably not heard of deep reinforcement learning — one of the most potent subfields of machine learning. And only a few people outside of the artificial intelligence universe know the distinction between deep learning and machine learning.
In this article, we’ll break down their differences, how they work and give you real-world applications of these technologies. Some of these examples, ironically, are things you use daily without knowing it. Let’s get to it.
Deep Learning (DL) vs Machine Learning (ML)
Machine learning and deep learning are the same, except that deep learning doesn’t rely on humans but on neural networks. Our brains’ network of neurons inspired the deep learning technique for machine learning.
Deep learning is a type of machine learning that uses programmable neural networks and doesn’t have to rely on human input. The goal of deep learning is to improve machine learning quality and help machines make more accurate decisions.
How is deep learning an improvement on machine learning, and how does it work?
What is Deep Learning?
To understand deep learning, you must understand neural networks. The design of deep learning models ensures it analyzes data using a logical structure that resembles how humans draw conclusions.
Neural networks imitate the neurons in the human brain using artificial neurons called nodes. These nodes are
- Input node or layer
- Hidden nodes or layers
- Output node or layer
These layers stack together, next to each other. Each node receives inputs from data, multiplies these inputs using random weights, calculates them, and includes a bias. The system then applies activation functions, which are nonlinear functions, to determine the discharge neuron.
Applications that run on deep learning models use algorithms known as artificial neural networks. These neural networks model how the human brain processes information, making them superior to how typical machine learning models function.
Getting deep learning models to replicate the human brain is tough, especially in making sure it doesn’t draw incorrect conclusions. However, functional deep learning — when it works — is considered the most important pillar of artificial intelligence.
One example of functional deep learning is Google’s AlphaGo (1) which learned to play the board game, Go. AlphaGo learned to play the game by playing against world-renowned masters of Go and become popular when it defeated these masters.
AlphaGo became proof that deep learning — done right — can become instrumental for solving extremely complicated problems that humans may not b capable of solving.
Deep learning models or algorithms accomplish different tasks. Thus we have different types o these models. The most common deep learning algorithms are listed below.
- Autoencoders (AE)
- Recurrent Neural Networks (RNNs)
- Generative Adversarial Networks (GANs)
- Convolutional Neural Networks (CNNs)
- Radial Basis Function Networks (RBFNs)
- Restricted Boltzmann Machines( RBMs)
- Self Organizing Maps (SOMs)
- Long Short Term Memory Networks (LSTMs)
- Deep Belief Networks (DBNs)
- Multilayer Perceptrons (MLPs)
Let’s explore what these deep learning algorithms mean.
1. Autoencoders: These algorithms copy their inputs to their outputs. It accomplishes that process by shrinking the input into a representation in latent space and then reconstructs that input using that representation.
The autoencoder deep learning model is useful in data visualization, specifically dimensionality reduction and denoising data. We have convolutional, vanilla, regularized, and multilayer autoencoders.
2. Recurrent Neural Networks (RNNs): Recurrent Neural Networks use time series or sequential data. RNN is handy for robot control, music composition, speech synthesis, and other time series-related uses.
3. Generative Adversarial Networks (GANs): Generative Adversarial Networks is a deep learning model that uses the training set it’s given to learn to generate new data from the same statistics as the training set. It then uses a discriminator to separate the generated data from the true data.
GANs find use in dark-matter research, text-to-Image translation, cartoon character generation, semantic-Image-to-photo translation, 3D object rendering, human face photograph creation, and many other image training applications. Developers can even use this model to turn an old video game’s low-resolution, 2D texture into a 4k image quality using image training.
4. Convolutional Neural Networks (CNNs): Convolutional Neural Networks (CNNs) used to be called LeNet when Yann LeCun developed it in 1988. This artificial neural network is also known as ConvNet, space-invariant or shift-invariant artificial neural networks.
CNN is useful for working on two-dimensional images but can also find use in one and three-dimensional images. The convolutional layers of a CNN are what make it different from the standard multilayer perceptron (MLP).
Convolutional Neural Networks find use in recognizing such characters as digits and ZIP codes. It can also be applied to spotting anomalies, processing medical images, predicting time series, identifying satellite images, and other image classification uses.
5. Radial Basis Function Networks (RBFNs): Radial Basis Function Networks are artificial neural networks that use radial basis functions as their activation functions.
RBFNs find uses in time-series predictions, regression, system control, and classification.
6. Restricted Boltzmann Machines (RBMs): Geoffrey Hinton invented the Restricted Boltzmann Machines. RBM is a generative stochastic artificial neural network that’s capable of learning the probability distribution over its set of inputs.
The building block of deep belief networks is the Restricted Boltzmann Machines. RBMs are useful in topic modeling, collaborative filtering, classification, feature learning, dimensionality reduction, and regression.
8. Self Organizing Maps (SOMs): Self Organizing Maps was developed by Professor Teuvo Kohonen. This artificial neural network helps with data visualization by using self-organizing artificial neural networks to reduce the dimensions of data.
Humans cannot visualize high-dimensional data, thus the need for data visualization. SOMs make data visualization possible.
8. Long Short Term Memory Networks (LSTMs): Long Short Term Memory Networks solves the short memory of RNN by being capable of learning and memorizing long-term dependencies. By default, LSTMs recall past information for long periods of time.
Due to its ability to store information for a very long time, LSTMs are useful for time-series forecasts. Long Short Term Memory Networks use a chain-like structure, where four nodes interact uniquely.
LSTMs have other applications apart from time series uses. They are useful for speech recognition, pharmaceutical research, music composition, and other fields where artificial neural networks need long-term memories.
9. Deep Belief Networks (DBNs): Deep Belief Networks are artificial neural networks or generative graphical models built from the Restricted Boltzmann Machines model. It’s made of multiple layers of latent, stochastic variables. DBNs have connections between nodes but no connections between units within the nodes.
Deep Belief Networks are useful for data recognition, like motion-capture, image, and video. However, looking at Google graph (2), we can safely assume that DBNs are falling out of favor with deep learning developers.
- Multilayer Perceptrons (MLPs): Due to their simplicity, a multilayer perceptron model is the best place to begin the deep learning journey. MLPs have fully connected input and output layers. This algorithm has multiple hidden layers but maintains the same number of input and output layers.
Applications like machine-translation, image-recognition, and speech-recognition software can be built with the help of multilayer perceptron models.
What is Machine Learning?
Not all machine learning is deep learning, but all deep learning is machine learning. Unlike deep learning, machine learning doesn’t necessarily rely on neural networks.
We have three types of machine learning models:
1. Supervised machine learning: Machine learning models that train themselves on labeled data set is supervised machine learning. So humans can input information that contains the data they want the model to process, and that information may even be classified as you want them to appear.
For example, a machine learning model intended to identify black dogs will need to get image inputs of various types of back dogs.
2. Semi-supervised machine learning: As the name implies, semi-supervised machine learning uses both labeled and unlabeled information in its model. A sample data set will be used from a larger pool of data, and then the model will work with both that labeled data set and the unlabeled ones.
This model solves the problem of not having enough labeled data set for supervised machine learning algorithms.
3. Unsupervised machine learning: Unsupervised machine learning focuses on finding useful patterns and relationships in a data set that humans might miss. It takes in lots of data and then relies on its algorithms to find features worthy of labeling, as well as sort and classify data in real-time. It does all of these things without needing human help.
Deep learning falls under unsupervised machine learning. But it can also come under semi-supervised machine learning. Unsupervised machine learning would benefit large enterprises and more than small businesses. Big companies generate, interact with, or use more data than a team of data scientists could ever label.
For example, Gmail processes 43% (3) of all email messages sent, and 304 billion (4) emails go out daily. Thus, Gmail would need unsupervised machine learning models to process data from that ocean of daily emails.
What’s the Difference: Deep Learning vs. Machine Learning?
To wrap it up, the difference between machine learning and deep learning comes down to three points:
- Deep learning builds layers of algorithms to form artificial neural networks. It then uses that neural network to learn from the data it has and then make intelligent decisions drawn from its trials and errors.
- Whereas other types of machine learning rely on labeled data sets to parse data, learn from the data it parses, and draw conclusions from what it learned from the data.
- Deep learning is a branch of machine learning, and both spaces come under the artificial intelligence (AI) field. Deep learning is the driver of the most human-like expressions of AI.
What is Deep Reinforcement Learning
Deep Reinforcement Learning or Deep RL is a branch of machine learning that brings deep learning and reinforcement learning together. Deep reinforcement learning supports software-defined agents to accomplish their goals by learning the best actions possible in virtual environments.
Software-defined agents can attain human-level control through deep reinforcement learning. Reinforcement learning is focused on achieving a specific, complex goal. The process uses rewards and punishments to reinforce learning in a system.
Reinforcement learning connects the effect of an action done now with its delayed but eventual outcome in the future. These algorithms operate in a delayed-return environment — like humans.
AlphaZero is an example of deep reinforcement learning at work. Without humans training it at all, this software is able to use deep learning and reinforcement learning to train itself and make intelligent decisions.
In explaining Deep RL, we will use some technical terms. Let’s get familiar with them:
- Agent: This is the entity that takes action. For example, a character in a video game or self-driving car.
- Action: This is a set of all actions that an agent can take. These actions are unique to the agent in question. For example, video game characters could move up, down, sideways, or diagonally, whereas the stock market could sell, buy, or perform any other relevant action.
- State: The present and concrete situation of an agent is its state. A state is a time and space an agent finds itself relative to other things like the reward, capabilities, and threats surrounding the agent.
- Reward: This is the feedback upon which the success or failure of the agent’s actions in a particular state is measured. For example, in football, an agent gets a score for or against it at every touchdown.
- Environment: The world or surroundings where the agent moves. The environment takes the state and actions of the agent as input.
- Discount factor: This is a way to spur short-term indulgence in the agent. The discount factor lowers the agent’s motivation to take action based on the reward. The discount factor impedes delayed gratification and makes immediate rewards have the same value as future rewards.
- Policy: This is an agent’s strategy for action in a particular state. Policies chart actions to states. Policies map actions that can deliver the most rewards to states.
- Value: Value is the anticipated long-term reward factoring in discounts instead of short-term rewards. The farther into the future a reward is, the more it’s discounted. The more immediate a reward is, the less it’s discounted.
- Q-value: Q-value is Value with the addition of one parameter — current time. The purpose of the Q-value is to map action-reward pairs.
- Trajectory: This is an array of states and the actions that influence.
Reinforcement learning assesses an action by the results it can produce. It’s a goal-oriented model that learns a series of actions to make sure an agent reaches its objective target or goal.
The Relationship Between Neural Networks and Deep Reinforcement Learning:
Reinforcement learning relies on neural networks as function approximator algorithms (5), especially when working with an action-space or state-space that’s too large to be fully known.
As you know from previous sections in this article, deep learning uses neural networks to process data. In reinforcement learning, neural networks would learn to map states to values or states-action pairs to Q-values.
You can think of deep reinforcement learning as the use of deep learning models in reinforcement learning. Deep RL helps an agent reach their goal by taking into account real-world situations where delayed gratification and unknown factors influence outcomes.
Real-World Uses: Deep Learning vs Machine Learning
The benefits of machine learning can be seen everywhere. From businesses that rely on them for their ad buys to cybersecurity firms that use them to prevent theft.
Businesses and professionals use deep learning models more frequently across different fields. For example, recurrent neural networks (RNNs) and the convolutional neural networks (CNNs) algorithms are instrumental in the developments happening in natural language processing (NLPs), self-driving vehicles, and computer vision.
1. Digital assistants: Digital assistants like Alexa, Google Assistant, and Siri, use natural language processing (NLP). This machine learning technology makes it possible for computers to process voice data and text and grasp human language like humans.
Natural language processing is useful in speech recognition software, text-to-speech software, and natural language generation. Applications like the GPS readily use NLP.
2. Contextual online advertising: Deep learning algorithms can appraise the page’s content, including the topic, author’s opinion, and information delivery, and use it to serve contextually relevant ads that match the visitor’s interests.
3. Fraud detection: Rules-based fraud-detection is flawed. These flawed systems are prone to false positives and hardly detect criminal use of compromised financial data. That’s the problem that machine learning regression and classification algorithms are solving.
4. Medical image analysis: Digital medical imaging has seen significant advancements over the years. There is more information to help with diagnoses, but human error is still present. That’s where deep learning algorithms come to play. They help extract intelligence from medical images to make medical diagnoses more accurate.
5. Recommender Systems: Services like Spotify, Amazon, Netflix, and other recommendation engines that offer “just for you” and “People also use/like/watch” personalization solutions rely on deep learning algorithms. In industries like entertainment, retail, travel, news services, and recruitment, these recommender systems are common.
6. Chatbots: Chatbots are probably one of the biggest deployments of deep reinforcement learning in the real-world. This technology brings together natural language learning with deep neural networks and pattern recognition algorithms to interpret text inputs and then deliver intelligent and suitable responses to queries.
7. Cybersecurity: Using machine learning, businesses and professionals can extract data from alerts, articles, reports, and other sources to gain intelligence, spot threats, brief security analysts, and respond proactively to potential security events.
8. Self-driving cars: Without machine learning, self-driving cars would not exist. These vehicles must identify objects in their environments, make predictions on the move, anticipate danger well ahead of time, respond to them, and be self-directed without needing human input until they arrive at their destinations. Self-driving cars would use just about every machine learning and deep learning model out there to help them perform these functions without error.
More industries: Machine learning and deep learning find uses in many other sectors, including supply chain and logistics, load balancing, traffic control, robotics, and many more.
Understanding the Union: AI, ML, DL, and Deep Reinforcement Learning
Through Artificial Intelligence (AI), humans are attempting to create thinking and intelligent machines that mimic human behavior or other living things. That quest has led to the development of machine learning.
Machine learning is a method by which computers learn from the data that humans feed them without relying on traditional computing rules. Computers that run machine learning algorithms would learn from datasets provided to them.
Typically, datasets for machine learning are labeled, but that’s not always possible. So computers need to learn from making connections between unlabelled datasets, just like humans can learn from observing their environments. That’s how deep learning was born.
Deep learning uses artificial neural networks that mimic the human brain’s neurons to process large amounts of data and then derive useful patterns from them.
Computers use reinforcement learning to train themselves to achieve goals that require multiple complex decisions by learning through trial and error, just like humans. If these decisions mimic real-life situations where uncertainties can’t be fully defined, deep reinforcement learning will come in handy.
Deep reinforcement learning helps computers use a blend of deep learning — processing data with artificial neural networks — and reinforcement learning, learning through trial and error.
Both businesses and professionals can benefit from machine learning. Medical professionals can rely on deep learning algorithms to interpret medical imaging better. Businesses can use chatbots to engage their audiences better on their websites.
There are no limits to the fields that can benefit from deep learning, machine learning, and deep reinforcement learning. These AI technologies have gone beyond being supplementary to existing industries.
Entire markets, industries, and product lines run on artificial intelligence. Self-driving vehicles, chatbots, and recommendation engines like Netflix and Spotify all rely entirely on deep learning and deep reinforcement learning to work.