McKinsey estimates that deep learning constitutes 40 percent of the overall economic impact from analytical techniques. That potentially translates to trillions of dollars in value. At the same time, the deep learning market has a tremendous 42 percent annual growth rate, which means that it’s doubling in size every two years.
Clearly, deep learning is vital to businesses and its importance is only going to increase. However, it can be challenging to understand what deep learning is and how it differs from other types of machine learning. This article will explore the key distinctions between deep learning and reinforcement learning and explain when each is most appropriate.
First, let’s define our terms. Deep learning is a subset of machine learning that uses neural networks to learn how to identify patterns in data. It is often used for tasks such as image recognition, natural language processing, and recommendation systems. In contrast, reinforcement learning is a type of machine learning that teaches agents how to make decisions in order to achieve a specific goal.
One of the key distinctions between deep learning and reinforcement learning is that deep learning is data-driven while reinforcement learning is goal-driven. With deep learning, the algorithms learn on their own by analyzing large amounts of data. In contrast, reinforcement learning requires feedback for the agent so that it can learn what actions lead to the desired outcome.
In this post we’re going to take a deep dive into understanding deep learning and reinforcement learning, how they’re different from each other and their respective applications. Plus, we’ll explore the easiest way to apply them to your business. To learn about Machine Learning in more depth you can check out The Complete Beginner's Guide to Machine Learning.
Deep learning, as the name suggests, involves large networks of neurons that can learn to recognize patterns in a dataset. This idea is loosely modeled after the way the brain works. Different layers in the network learn different aspects of the data, and the output of the final layer is a prediction of the desired outcome.
Today’s deep learning models are based on supervised learning, which means that for every datapoint, there’s a corresponding label. Since deep learning models learn from large amounts of data, and are often deployed on huge amounts of new data in real-time, they require a lot of computing power.
Traditionally, this complexity meant that businesses would have to hire computer science and data science experts with skills like Python, and even set up their own GPU clusters. Nowadays, any business user can use no-code tools like Akkio to effortlessly build and deploy these models. Simply put, you don’t need to be a data scientist to use deep learning.
Deep learning is commonly used for optimization tasks like facial recognition systems, self-driving cars, and speech recognition. Deep learning networks can be very large and require a lot of data to train accurately, so they're widespread in enterprise applications like Siri and Google Assistant.
Further, deep learning fuels the latest advancements in natural language processing and computer vision. All these advancements have led to more widespread AI adoption in the commercial world.
This is because more data often leads to better results with AI models. And with the amount of data being generated every day increasing exponentially, businesses are turning to deep learning to help them make sense of it all.
Reinforcement learning, on the other hand, is a type of AI that teaches computers how to learn from experience. In reinforcement learning, agents, or computer programs, earn rewards for making correct decisions and punishments for incorrect ones.
Unlike deep learning, reinforcement learning is not fully supervised. That said, it’s not quite unsupervised learning either, since “labels” are created through trial and error interactions with the environment.
This approach is used for tasks from playing Atari board games to training robots. Reinforcement learning is particularly well-suited for tasks where it’s difficult to design a good set of rules ahead of time, since the computer can learn from its mistakes and adjust its behavior accordingly.
Google's AlphaGo is a good example of reinforcement learning in action. After being initially trained on data from thousands of Go matches between human players, AlphaGo was able to beat the world’s best Go player by playing against itself millions of times and learning from the results.
Google’s DeepMind team created AlphaGo using a reinforcement learning algorithm called Q-learning, which is a “value-based” method in which the algorithm is rewarded for correct actions. There are several other reinforcement learning methods, including policy-based and model-based learning systems.
While not as widely known, reinforcement learning is also used in a variety of industrial and commercial applications. For example, reinforcement learning recently prevailed against a real veteran F16 human pilot in a series of dog fights, and it is being used to help robots learn how to manipulate objects. As reinforcement learning algorithms continue to get better, we can expect to see even more impressive and practical applications in the future.
Put simply, deep learning is a method of machine learning that uses large neural networks to learn how to perform a task.
Reinforcement learning, on the other hand, is a type of machine learning that allows machines to learn how to maximize a certain outcome, like profit or engagement, by trying different actions and seeing how they affect the outcome.
Let’s look at how and why you should apply these techniques.
As you now know, deep learning is a branch of machine learning that is based on artificial neural networks. These networks are composed of layers of nodes, which are interconnected and can learn to recognize patterns. Deep learning networks can be trained to recognize complex patterns in training data, including images, text, and sound.
In finance, deep learning has been used to predict stock prices, identify fraudulent transactions, develop trading strategies, and ultimately optimize the decision process in a number of areas.
One particularly promising application is sentiment analysis, which uses deep learning to detect positive or negative sentiment in text data. This can be used to predict stock prices or make investment decisions.
RenTec, the infamous investment fund producing unrivaled annual returns north of 70%, uses machine learning to enhance their ability to forecast stock movements.
RenTec's Medallion Fund has been called a "$74 billion money-making machine like no other," and it's clear that machine learning is a big part of their success. With such high returns, it's no wonder that other investment firms are trying to replicate RenTec's model.
No other firm, however, has been able to produce similar results. This is in part due to RenTec's access to better data, more powerful computers, and their team of world-class data scientists. In short, they're able to take advantage of machine learning in a way that other firms simply can't.
There are many potential applications of deep learning in healthcare, including diagnostics, treatment planning, patient monitoring, and drug discovery. One of the most promising uses of deep learning in healthcare is in the area of cancer detection and treatment.
Researchers are using deep learning algorithms to identify unique patterns in cancer data that can help with diagnosis, treatment planning, and prognosis. In one study, deep learning was used to create a model that could distinguish between different types of breast cancer with 99% accuracy. Another group of researchers used deep learning to develop models that could predict how long a patient with cancer will live.
Deep learning algorithms can also be used to improve patient care. For example, they can be used to monitor patients' vital signs and send notifications if there is any deviation from normal. This can help doctors catch problems early, before they become serious.
Deep learning algorithms are also being used to improve drug discovery. In one study, a deep learning algorithm was used to identify new potential drugs.
The traditional drug discovery process is expensive, time-consuming, and often yields disappointing results. However, deep learning promises to speed up the process and improve its accuracy. This can have real-world impacts, such as getting potentially life-saving drugs to patients faster.
With the costs of healthcare increasing and the need for new treatments rising, deep learning in drug discovery is a field with a lot of potential.
Deep learning can be used to improve customer segmentation, identify new prospects, and develop predictive models that can help businesses anticipate customer needs. It can also be used to improve the accuracy of predictions made by marketing automation tools.
Consider subscription services, which are constantly looking to boost customer retention rates. Deep learning could be used to analyze past customer behavior in order to identify which customers are most likely to cancel their subscriptions. This information could then be used to create targeted marketing campaigns designed to keep these customers from canceling.
Deep learning can also be used to create models that predict customer lifetime value. These models can then be used to prioritize prospects and optimize marketing spends.
Marketing teams are already using deep learning to improve their online advertising efforts. By analyzing clickstream data and customer purchase histories, they can develop models that predict which ads are most likely to result in conversions. They can also use deep learning to target ads to specific customers based on their past behavior.
The potential benefits of deep learning for marketing are endless. With the right tools and data, businesses can use deep learning to create custom marketing programs that improve customer loyalty, boost revenue, and anticipate customer needs.
There are several reasons why reinforcement learning is a valuable tool for businesses. Firstly, it can automate repetitive tasks, saving time and money. Secondly, it can help identify and optimize key business processes. Finally, it can improve decision-making by providing valuable data and insights.
As McKinsey reports, reinforcement learning was recently used by the reigning champion of America’s Cup 2021, in order to design a more performant boat, with ideal hydrofoils that helped it beat the competition.
Reinforcement learning is also being used to develop more efficient algorithms for cooling data centers. By using reinforcement learning, one firm was able to reduce the amount of energy used by its data center by 15%.
With irreversible climate change becoming an increasingly pressing global issue, firms are under pressure to find ways to reduce their carbon footprints. Reinforcement learning is one tool that can be used to help achieve this goal.
It’s also used in a variety of industry applications, including:
Further, many advertising businesses use reinforcement learning to optimize their campaigns. By analyzing past customer behavior, they can identify what ads are most likely to result in a sale and then use reinforcement learning to tweak and improve those ads. This helps them to achieve the best possible return on investment for their advertising budget.
Reinforcement learning can also be used in finance. IBM, for instance, has developed a reinforcement learning tool that can help investors make better stock choices. The tool analyzes a large number of data points, including past stock prices, company earnings, and analyst ratings, to identify patterns and trends. By doing so, it can provide investors with insights that can help them make more informed decisions about which stocks to buy and sell.
While deep learning learns from real-world data, reinforcement learning learns from synthetic data as an agent interacts with an environment, receiving feedback based on its actions.
There’s a lot of hype around artificial intelligence, and for good reason. AI has the potential to transform industries, making processes more efficient and customer interactions more personalized. However, many businesses are hesitant to adopt AI because they believe it requires extensive coding knowledge.
This is no longer the case. Thanks to Akkio, businesses of all sizes can harness the power of AI without any coding experience. Akkio is a self-service platform that enables users to build and deploy machine learning models without a single line of code.
With Akkio, you can:
Akkio’s machine learning algorithm automatically detects patterns in your data and builds models accordingly. This means you don’t have to spend time pre-processing your data or selecting the right algorithms.
Once your model is built, you can deploy it in minutes. This means you can start seeing the benefits of AI in your business right away.
Most machine learning models take weeks or even months to produce results. With Akkio, you can get results in days, not weeks. This is thanks to Akkio’s parallel processing capabilities, which allow you to process large amounts of data quickly.
Finally, Akkio’s scaling capabilities enable you to grow your models as your needs grow. This means you can continue to reap the benefits of AI long into the future.
Deep learning and reinforcement learning are two of the most popular types of AI. Deep learning is a method of machine learning that enables computers to learn from big data, whereas reinforcement learning is a type of machine learning that allows machines to learn how to take actions in an environment so as to maximize a reward.
Both deep learning and reinforcement learning have their advantages and disadvantages. For example, deep learning is good at recognizing patterns in data, whereas reinforcement learning is good at figuring out the best way to achieve a goal. However, deep learning is also more complex and can be more difficult to configure and use, while reinforcement learning is more straightforward but may not be as effective at recognizing patterns.
Akkio's no-code AI makes implementing these systems, and other AI types, much easier. Akkio is a platform that allows users to build and deploy machine learning models without any programming. This makes it easy for anyone, regardless of their experience with coding or machine learning, to use AI. Sign up for a free account to get started.