A Simple Breakdown of Machine Learning vs. Deep Learning

Machine Learning vs. Deep Learning | TurnKey

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.

Table of Contents

Basics of Machine Learning

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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Practical Use Cases of Machine Learning

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:

Use CaseSummaryCompanies or Products That Use It
Email FilteringML algorithms identify and filter out spam emails.Gmail, Outlook
Fraud DetectionSupervised learning techniques detect fraudulent activity in banking and finance.PayPal, ZestFinance
Product RecommendationsCollaborative filtering, a machine learning method, is used in recommender systems.Amazon, Netflix
Predictive MaintenanceML identifies patterns in machine data to predict failures and schedule maintenance.GE Aviation, IBM Watson IoT
Medical DiagnosisML algorithms help in diagnosing diseases based on symptoms.Zebra Medical Vision, Google's DeepVariant

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?

Basics of Deep Learning

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.

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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.

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A simple artificial neural network. Source: Levity

What are the different types of deep learning algorithms?

There are various types of deep learning algorithms, each designed for specific tasks and data types. Some common types of deep learning algorithms include:

  • Feed-forward neural networks: These are the most basic type of deep learning networks, where information flows in one direction, from input to output, without any loops or feedback connections. They were first mentioned in scientific literature in the 1950s and are foundational to many modern AI applications.
  • Convolutional neural networks (CNNs): These networks are designed to automatically learn and extract spatial hierarchies of features from input data and are commonly used for image and video processing tasks. CNNs (and I don’t mean the 24-hour news network) found their widespread application in the ImageNet competition, an image recognition contest.
  • Recurrent neural networks (RNNs): RNNs are suitable for sequential data, such as time series or natural language processing tasks, as they can capture temporal dependencies and have feedback connections that allow information to persist over time. A famous application of RNNs is Google's Smart Reply feature, where RNNs help generate automatic email responses.
  • Generative adversarial networks (GANs): GANs consist of two neural networks, a generator and a discriminator, that compete against each other to generate realistic synthetic data. One of the most well-known applications of GANs is the creation of AI-generated artwork, like "Portrait of Edmond Belamy," which sold for $432,500 at auction in 2018:
Edmond de Belamy
  • Long Short-Term Memory (LSTM) networks: LSTM networks are a type of RNN that can effectively capture long-term dependencies in sequential data by using memory cells and gating mechanisms. LSTMs were introduced by Hochreiter & Schmidhuber in 1997 and have since been a major component in many successful applications of deep learning, such as speech recognition systems like Google's Voice Search.

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Practical Use Cases of Deep Learning

These practical use cases of deep learning demonstrate its wide-ranging applications across industries, transforming fields like healthcare, transportation, communication, and entertainment:

Use CaseSummaryCompanies or Products That Use It
Image RecognitionConvolutional Neural Networks (CNNs) are used to recognize images and classify them into categories.Google Photos, Facebook's DeepFace
Speech RecognitionDeep learning techniques are used to convert spoken language into written text.Apple's Siri, Amazon's Alexa
Natural Language ProcessingDL is used to understand, generate, and translate human language.Google Translate, Microsoft's Xiaoice
Autonomous VehiclesDeep Learning algorithms help vehicles understand their surroundings and make driving decisions.Tesla Autopilot, Waymo
Medical Imaging AnalysisDeep Learning is used to analyze medical images and identify patterns related to diseases.Zebra Medical Vision, PathAI

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.

Key Differences Between Machine Learning and Deep Learning Expertise

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.

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Machine learning vs. Deep learning. Source: Quantdare

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.

7 Crucial Factors When Choosing Machine Learning or Deep Learning Specialists

7 Crucial Factors When Choosing Machine Learning or Deep Learning Specialists

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:

  • Mission Specifics: The difference between machine learning and deep learning becomes clear when you identify your unique task. ML, a subset of artificial intelligence, can handle a wide variety of tasks. However, DL is a heavyweight when it comes to tackling complex tasks involving unstructured data, such as image recognition, speech recognition, natural language processing, and computer vision.
  • Data Landscape: Deep learning models need large amounts of data to perform at their best. On the other hand, standard machine learning models can deliver results even with smaller data sets.
  • Hardware Capabilities: Deep learning vs machine learning often boils down to the computational resources available. Deep learning algorithms require robust hardware, such as GPUs, to effectively run their course.
  • Talent Capabilities: Assess the pool of talent for ML and DL expertise. Remember, the world of deep learning applications is a specialized niche within the broader landscape of machine learning. Mastery of artificial neural networks and complex deep learning algorithms is paramount.
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Overlap of skills between Data-driven roles. Source: edx
  • Model Transparency: The deep learning model's notorious reputation as a "black box" can be an issue if your project requires high interpretability. Machine learning algorithms, on the other hand, provide a more transparent view of the learning process.
  • Scalability Quotient: When considering machine learning vs deep learning, think about scalability. Deep learning models, with their computational intensity, might not scale as seamlessly as machine learning models for large-scale deployments.
  • Time and Budget: Another difference between machine learning and deep learning lies in time and resource investment. Training a deep learning model often demands more time and money compared to more economical machine learning models.
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Data roles and technologies used. Source: Ryan Swanstrom, Data Science 101

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?

How Difficult Is It to Find Machine Learning or Deep Learning Engineers?

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:

  • Expertise. Our team extension service allows us to integrate top talent into your existing team quickly. This improves efficiency, helping you meet your goals faster.
  • Offshoring. We also provide offshoring solutions, offering access to a diverse and expansive pool of tech specialists at a reduced cost. Our connections extend to Eastern Europe and Latin America, ensuring a vast pre-vetted selection of top talent.
  • Retention. Retention is key to a successful team. Our proven retention program cuts churn by over 50%, keeping valuable ML/DL engineers on your team for the long term.
how to reduce churn

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!


What is the difference between deep learning and machine learning?

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.

What's the difference between deep learning and neural networks?

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.

August 11, 2023

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