With this advanced era of technology, everything is possible with just the click of a button. Have you imagined how? How do self-driving cars work, or how do virtual assistants like Google Assistant Alexa help us with tasks? All this is because of data science – machine learning, deep learning, and many more. To some people, it may feel like an extremely intimidating area. How can machines learn and act like humans? And Why would we want machines to do so?
They have become an indispensable part of our day-to-day life. With advancing technology, more and more new features and designs come up. One such feature is acting human-like. These technologies help us make our daily chores easier and continue to fascinate us in different ways.
Most of us are familiar with the term AI, i.e. Artificial Intelligence and machine learning, but lesser people know about deep learning. Deep learning is also a field of data science that comes under machine learning and AI.
Let\’s find out more about deep learning and how it is a subset of machine learning, which is a subset of AI.
We must first know about artificial intelligence and machine learning to understand deep learning.
Artificial Intelligence: As the word says, artificial – something that is man-made and intelligent – the ability to think, learn, and understand. Artificial Intelligence is the ability of machines like robots computers to act and perform tasks like human beings.
It is a field of data science that constitutes machine learning, speech processing, natural language processing, neural networks, robotics, evolutionary computation, and many more. All these sound too complex to understand for a simple human. But these complex together makes our life easier.
AI has seen an immense rise in the past few years due to an improvement in computing storage and power, advanced algorithms, and increased volumes of data; processing has become easier. Earlier AI research was limited to problem-solving computer training for basic human reasoning, but this research paved the way for new and improved features and functionalities.
Need for artificial intelligence today
Analysis and deep-diving into data: AI uses self-learning algorithms making data itself an asset. This data is a big advantage when it comes to competition among industries. The better your data is, the more advantage of getting better results.
We can now add hidden layers for extra security build fraud detection systems using big data and computer power.
Accuracy: It is now possible to achieve incredible accuracy using deep neural networks, a sub-field of AI. The more you use an AI-based product, the smarter and more accurate it gets. For example, the more your interactions with Google, Alexa the more they become personalized for you. It has also proven to be very helpful in the field of medical sciences. For example, it is now possible to detect cancer on images with great accuracy with AI.
Intelligence: With AI, we can create new intelligent products and add intelligence to existing products. Combining large amounts of data with bots, smart machines, automation, and conversation platforms improves the technology. Some of the examples are Siri in Apple products, Alexa and smart home appliances, smart cams to analyze investments.
Process automation: AI uses high volume frequent computerized tasks for progressive learning. This automates the process and thereby reduces human intervention. Algorithms acquire skills with regularities and structure. Example: Computers can teach themselves how to play chess.
These wonders are possible due to the various sub-fields of AI. One such sub-field is machine learning.
Machine Learning: The meaning is in the name itself. It is a branch of AI and data science that makes machines learn by themselves, using algorithms and data. Just like AI, machine learning has evolved a lot over the years. Machines can now learn, analyze data, recognize patterns, and make decisions based on this analysis. In addition, repetitive processing and refining of data give more reliable results, leading the algorithms to adapt to new changes.
Machine learning has now made it possible to apply complex calculations to big data. Have you ever thought about how you start getting advertisements while surfing, based on the recent products you were looking for? How do you get recommendations on OTT (Over-the-top) platforms? This is all due to machine learning algorithms working behind these platforms, behind Netflix, Google, Amazon, and many other giant companies.
Why is machine learning so important in today\’s world?
Machine learning finds its main usage in commercial industries that work on big data mostly collected from their consumers or customers. However, for small businesses to scale, they need to improve and automize processes which are done with the help of machine learning.
Recommendation systems and fraud detection systems largely use machine learning algorithms, making our complex tasks easier.
The main applications using machine learning are:
Government: Data mining is largely required in this sector for fraud detection and for security purposes to minimize identity theft.
Financial Services: This industry mainly involves cyber-surveillance for fraud, identifying investment opportunities, and helping the investors predict and know the trade.
Health Care: With the invention of wearable devices and in-built sensors, the health care field has been using this data to analyze patients health and reduce risks.
Gas and Oil: This industry is still expanding in its usage of machine learning. Current usage involves:
- Streamlining the process of oil distribution.
- Analyzing the grounds for minerals.
- Sensor failure in refineries.
Transportation: Google maps, one of the most commonly used applications, uses machine learning to find the best routes. This industry also used ML to analyze different routes and predict prior problems to increase profitability.
Retail: Giving a personalized shopping experience to each and every user is possible only with the help of ML. User data is captured and analyzed for different trends and patterns; accordingly, marketing campaigns are targeted.
Machine learning itself comprises sub-fields that make all of the above things possible. A combination of these sub-fields results in wonders for humans. One such sub-field is deep learning – a subset of machine learning.
Deep Learning: or deep structured learning, is a field of data science that comes under machine learning. In simple terms, deep learning is diving deep using a number of layers into learnings of machines that teach them to do what is natural to humans. It is a specialized form of machine learning and is largely inspired by the brain\’s functioning. In this, computer models are trained in such a way that they classify information from texts, images, and audio. This is how it becomes a subset of machine learning.
Deep learning is booming quite a lot, all because of the wonders done by it. From self-driving cars to robots, voice controls in tablets, phones, TVs, and more.
Today, deep learning matters a lot just because of its accuracy. It has achieved accuracy in recognition in levels higher than before. This is helping producers to meet consumers\’ expectations by turning them into reality. Moreover, it has been outperforming itself over the years, so much so that it has given better results than humans in some tasks.
With the availability of large amounts of data and computing power, it has become so popular now. Therefore, two of the main requirements of deep learning to work efficiently is computing power and labelled dataset, which is collected, classified and stored without any problems.
Applications of deep learning include:
Text generation: To generate new text with proper grammar and spelling using a piece of text.
Aerospace and military: To classify and identify space objects using data collected from satellites. This helps in classifying an area as safe or unsafe for military troops.
Customer experience: To improve customer satisfaction by giving them personalized offers. To help customers with issues, automated chatbots are also used.
Industrial automation: For providing safe environments and services, for improving the safety of workers by detecting the range of safety near the machines.
Medical research: To detect cancer cells automatically and proactively identify health risks.
Computer vision: in image classification, object detection, and segmentation and restoration
Electronics: for automatic speech and hearing translation. For example, home assistants like Alexa.
Automated driving: to detect traffic lights, road signs, and pedestrians and to reduce the accidents
Don\’t you want to know how deep learning works and what makes it so efficient? Then, let\’s find out how.
Neural network architecture is the answer. Deep learning is also known as \’Deep Structured Learning\’ because of the neural networks involved in it. Earlier neural networks could only have 2 to 3 layers, while deep neural networks can now have up to 150 hidden layers for processing.
Deep learning models are trained with the help of large labelled datasets and neural network architecture. Here, manual extraction of features is not required as neural networks can itself learn the features directly.
The methods used to create these deep learning models are as follows:
Transfer learning: is the process of improving an already trained model to perfection. First, data is fed to the system then some adjustments are made. After perfection, new data is fed to get the results. This reduces the computation time.
Training from scratch: is the process of collecting data, configuring network architecture, and then training the model. This training takes much more time than other techniques due to large amounts of data.
Learning rate decay: Learning rate defines the conditions required for operation prior to the process of learning. High learning rates result in unstable training, and low learning rates result in a lengthy process.
Learning rate decay is a process of adapting the learning rate such that it increases the performance and reduces the time taken in training.
Dropout: is the process of solving overfitting with a large number of parameters. Units and the connection with the neural network are randomly dropped during training, improving the performance of neural networks.
Limitations of deep learning:
- Getting accurate results from trained models can be tough in the case of small datasets or data from a single source. This happens because models get a narrow training area with small datasets; we get the results based on what it learns.
- With biased datasets, models get trained incorrectly. That is, it reflects the bias learned in its predictions.
- Models learn with subtle variations; data cleanup becomes an issue in this case as unclear data leads to incorrect training, which further leads to incorrect outcomes/ predictions.
- Learning rate in the learning rate decay method becomes a major issue if it fluctuates or is too high or too low. Low learning rates can also break the flow and get the systems stuck.
- There are also some hardware limitations, such as multicore high-performing GPUs (Graphics Processing Units) use a larger amount of energy and are expensive. Deep learning requires high RAM (Random Access Memory), SDD (Solid State Drive), and hard disk.
- Deep learning requires large and powerful data that is labelled as well.
As technology is growing so fast, so is the field of data science. We will soon be able to overcome these limitations. But, as of now, what matters to us is the end result so long it does not harm us.
As a subset of machine learning, deep learning has proven to be a very advanced and powerful technology in today\’s world. It has also become a vital element for data science due to its vast applications and rapidly growing research. Today, we have driverless cars, automated home assistants, trade analyzers, fraud detection systems, and recommendation systems, all because of deep learning, machine learning, and AI.
When used for good, technology is a boon, so let\’s make it a boon for humankind. Let\’s use it for good and let it advance as you never know how far this technology may go.
I hope this article helped you dive deep into deep learning and understand how it has emerged from Machine Learning, which has emerged from Artificial Intelligence and why these technologies are so important for us today.
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