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Deep Learning and Machine Learning may seem like interchangeable buzzwords floating around in the vast cosmos of Artificial Intelligence. However, that’s far from reality. If you’re on a quest to understand or recruit in the AI field, it’s crucial to grasp these terms and their distinctive roles. Fret not, I’m going to break them down so they are not as daunting as some tech pundits suggest.
As we embark on this virtual voyage to demystify these concepts, I’ll decipher the terminologies, and distil the jargon, to bring clarity to these transformative tech wonders that are reshaping our world.
Let’s start with the basics.
Artificial Intelligence (AI) has been a hot topic for quite some time. As a broad field of computer science, AI is about creating intelligent machines capable of performing tasks that normally require human intelligence. Think speech recognition, decision-making, and even language translation.
Now, let’s get to the first course of this tech feast: Machine Learning. ML, a key subset of AI, operates on the principle of learning from experience. In scientifical language, Machine learning works by training algorithms on a dataset to learn patterns and relationships in the data, and then using that knowledge to make predictions or decisions about new data. The process involves selecting and preparing data, choosing an appropriate algorithm, training the algorithm on the data, and then evaluating the algorithm’s performance on new data.
Imagine ML as a kid learning to ride a bike. You don’t tell the kid to adjust their balance by 15 degrees or rotate the pedals at a certain speed. They learn by doing, by falling off, getting back on, and gradually figuring out what keeps them upright. That’s pretty much how ML algorithms work – they learn patterns from data, make a bunch of mistakes, adjust, and eventually get it right without being explicitly programmed.
When it comes to machine learning, we’ve got three main types: supervised learning (where we give the algorithm labeled data and a clear goal), unsupervised learning (where the algorithm has to make sense of unlabeled data), and reinforcement learning (where the algorithm learns from feedback, kind of like a dog being trained to fetch).
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There are plenty of practical use cases of machine learning that demonstrate its wide-ranging applications across industries, transforming fields like finance, healthcare, transportation, communication, and entertainment:
Great, right? But you are likely wondering how SME are use this technology. Our dear client, iScribe Health, a leader in medical transcription within orthopedic medicine, uses ML to allow its clients to choose from a combination of virtual scribes, speech-to-text, AirMic, and transcription – with all services powered by a single device.
It’s clear that machine learning will continue to advance and contribute to innovative solutions in various domains. So how does Deep Learning stack up?
Alright, it’s time to continue our tech journey into the basics of Deep Learning (DL). If Machine Learning was a bike, Deep Learning is like a fully-equipped, self-driving car.
Actually, Deep learning is a subset of machine learning. Think of it as the “special forces” division of the ML world. It involves training artificial neural networks on large volumes of data. Neural networks mimic the structure and function of the human brain and can identify patterns and relationships in data. That’s right, they’re not just creating intelligent machines, they’re building digital brains!
Now, these neural networks are stacked up in layers, creating a sort of data highway. Each layer performs a complex operation, picking out particular patterns from the data, kind of like how we spot shapes in clouds. The more layers, the more complex and abstract the features the network can recognize. They call these stacked layers of neural networks deep neural networks (DNNs).
The beauty of DL is that it gets better as the data gets bigger, making it the superstar when it comes to Big Data. This is why DL excels at tasks like image recognition, speech recognition, and natural language processing, where data can be highly unstructured and complex.
So what’s the big deal with big data? Well, think of big data as a library the size of ten football fields, all for our ML algorithm-kid to learn from. The more data we have, the better the machine learning algorithm can learn and solve complex problems.
How does deep learning work, you ask? Let’s stick with our self-driving car analogy. Imagine you’re in the car for the first time and it’s learning the route from your home to your favorite coffee shop. It makes a wrong turn and needs to do some course corrections. But once it knows the route, then every trip, it adjusts its path based on traffic, road closures, and your coffee cravings. This adjusting and learning process, called backpropagation, helps the DL model improve over time, much like our self-driving car becoming an expert at navigating to the coffee shop and ensuring you never get in an accident.
There are various types of deep learning algorithms, each designed for specific tasks and data types. Some common types of deep learning algorithms include:
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These practical use cases of deep learning demonstrate its wide-ranging applications across industries, transforming fields like healthcare, transportation, communication, and entertainment:
Deep learning continues to advance and contribute to innovative solutions in various domains.
So now that you have a foundation of these two terms, let me share their key differences.
Let’s dive straight into the meat of the matter. In the arena of AI, machine learning and deep learning are two crucial players, each with unique abilities that make them an asset to any tech team. However, to bring the right talent on board, it’s essential to understand the differences between the two.
Machine learning is a form of AI that equips computers with the ability to learn from data, utilizing machine learning algorithms to execute tasks without explicit programming. These machine learning models are rather adaptable, capable of training on smaller data sets, akin to a chess player who can learn a wide array of strategies from just a few games.
On the other side of the coin, we have deep learning. Deep learning is a specialized subset of machine learning, emulating the learning process of the human brain using artificial neural networks. These deep learning models necessitate vast quantities of training data to operate effectively, making it the tech equivalent of a grandmaster chess player who has played thousands of games, being able to decipher complex and intricate maneuvers.
To put it succinctly, while machine learning can work wonders with small datasets, performing tasks like spam filtering and fraud detection, deep learning excels in areas that require intensive data analysis. This includes applications like image recognition, speech recognition, and natural language processing.
For me, it’s like comparing a Swiss Army Knife to a high-powered microscope. Both are effective tools depending on the task, but you wouldn’t use the microscope to open a bottle of wine, would you?
Rest Assured – With TurnKey Labs by Your Side, Navigating the World of AI Recruitment is No More Difficult Than Using That Swiss Army Knife!
For this next section, I tapped into our most reliable resource to provide you with concrete and practical advice—TurnKey’s very own tech recruiting team, and I’ll share their invaluable insights on how to determine the essential players for your team.
When it’s decision time in the tech recruitment arena and you’re choosing between a machine learning (ML) ace or a deep learning (DL) virtuoso, several key differences come into play:
Taking all these factors into account will empower you to make an informed decision when choosing between ML and DL wizards based on your project’s unique requirements.
Now that you have a clear idea of who you need, you’re probably wondering: How am I going to find them?
Finding skilled Machine Learning (ML) and Deep Learning (DL) engineers is challenging due to high demand in the tech industry. TurnKey simplifies this process. We specialize in custom recruiting and pre-screening potential candidates for their technical prowess, thanks to these three factors:
In essence, TurnKey excels in Machine Learning talent acquisition and ensures you always have the right technologists on your team. So, whether you need extra horsepower or that very special horse, we’ve got you covered!
Deep learning is a subset of machine learning that utilizes complex neural networks to automatically learn and extract patterns from data, while machine learning involves training algorithms to identify patterns and make decisions based on data. Deep learning is more suitable for complex tasks and unstructured data, while machine learning is more versatile and can work with smaller datasets.
Deep learning is a subset of machine learning that utilizes artificial neural networks with multiple layers to learn and extract complex patterns from data. In other words, deep learning is a specific approach within the broader field of machine learning that focuses on training deep neural networks. Neural networks, on the other hand, refer to the computational models inspired by the structure and function of the human brain, which can be used in various machine learning techniques, including deep learning. In essence, deep learning is a methodology that employs neural networks with multiple layers to achieve powerful learning capabilities and solve complex problems.
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