Machine Learning vs Deep Learning: Which one to choose?

Machine Learning vs Deep Learning: Which one to choose?
N-iX
2020-04-07T20:24:35+00:00

Machine learning and Deep Learning are often used interchangeably. But that’s true only to some extent. So what do these two concepts have in common? What makes them different? And which one is better to choose for your specific business case?  In fact, deep learning is machine lea...

Machine Learning vs Deep Learning: Which one to choose?
By Ivan Hetman April 07, 2020

Machine learning and Deep Learning are often used interchangeably. But that’s true only to some extent. So what do these two concepts have in common? What makes them different? And which one is better to choose for your specific business case? 

In fact, deep learning is machine learning, but a better and more advanced one.

Deep learning is a subset of machine learning and it functions in the same way as machine learning. However, its capabilities and business cases it is applied to are a bit different. Now let’s look a bit closer at these two notions.

What is machine learning and when is it used?

Machine learning is the science of getting computers to act by learning from experience without being explicitly programmed. It is used in cases where it is difficult or infeasible to develop a conventional algorithm for effectively completing the task. Machine learning algorithms are used in a wide range of applications, such as market segmentation, prediction of equipment failures, email filtering, driverless cars, and more. Machine Learning is used for optimizing and automating a large share of tasks in almost any department: marketing, financial, HR, customer operations, and more.

How does Machine Learning work?

Data scientists train machine learning models with existing datasets, test the models, fine-tune them, and then apply well-trained models to real-life situations. The more data you feed when training the model - the better, and the more accurate results it will produce. 

The model runs as a background process and provides results automatically based on how it was trained. Data scientists can retrain the models as frequently as required to keep them up-to-date and make the results more effective. That’s why machine learning models improve their results progressively. For example, our partner Mercanto retrains machine learning models every day.

 Based on how the models are trained, they fall into 3 key categories.

Types of Deep learning and Machine learning 

Supervised learning - when the model is getting trained on a labeled dataset.

Example: analyzing customer data (age, gender, hobbies, etc.) and historical data to predict if a person clicks on an ad or not. 

Unsupervised learning - when the model is trained on neither classified nor labeled data, and the model has to find the dependencies in the data on its own. 

Example: market segmentation. 

Reinforcement learning - is a type of machine learning technique that allows a model to learn in an interactive environment by trial and error using feedback from its own experiences. In short words, the ultimate goal is to find the most suitable sequence of actions that would allow getting the reward - e.g winning a game. 

Examples: driverless cars, games.

How is deep learning different from conventional machine learning?

Deep learning vs Machine Learning

The main difference between ML and deep learning is that while standard machine learning models do make insights without being explicitly programmed and improve their results progressively, they still need some guidance and adjustments from humans. Whereas, deep learning relies on neural networks. 

''A neural network is a series of algorithms that recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. They can adapt to changing input; so the network generates the best possible result without the need to redesign the output criteria''. - Investopedia.

That means that in a deep learning model, an algorithm can determine if a prediction or a result is accurate or not through its neural network and it can adjust the parameters on its own if needed to improve the results. 

However, the downside of it is that they are more opaque and less explainable than conventional Machine learning models. 

Another important difference is that Deep learning models help to solve the most complex tasks and drive insights from an ongoing stream of unstructured data ( videos, texts, sensor data, images, etc.).

Deep learning models and neural networks are used for enabling self-driving cars, speech recognition, image recognition, natural language processing, precision medicine, and more. 

Many big businesses across a wide range of industries that use Machine learning are in fact powered by Deep learning, such as: 

Netflix - for their advanced recommendation system 

JP Morgan Chase - for insider trading detection and government regulatory compliance

Pinterest -  for discovering visually similar objects, colors, patterns and more

Baker Hughes  - for seismic modeling, automated well planning, predicting machinery failure, and optimizing supply chains, and many more.

Via - uses deep learning to optimize ride-sharing routes.

Deep learning is also used for trying to solve the most challenging healthcare problems. It is being widely used to try to curb the Cobid-19 pandemic. For example, Google’s DeepMind published research discussing how they used deep learning to predict the structure of proteins associated with SARS-CoV-2, the virus that causes COVID-19.

COVID-Net,  a convolutional deep neural network, was built to diagnose COVID-19 using chest x-rays of patients with different lung conditions, including COVID-19.

When should your business use Deep learning?

Though Deep Learning is seen as a more advanced type of machine learning and it helps to solve more complex tasks, it is not a good fit in all business cases. 

First of all, to make sure Deep learning Models are effective you need to train them on enormous amounts of data, like terabytes and petabytes of data. That’s why Deep learning is typically a good option for businesses working in a financial sector and banking, e-commerce, telecom, social media, oil&gas, customer support, and other domains that deal with an ongoing stream of big data.  

If you don’t have very large datasets for training, deep learning models will produce highly inaccurate and sometimes even absurd results. Besides, the more data a trained Deep Learning model will deal with daily, the more advanced results and betters performance it will achieve progressively. Here is an image to illustrate.

deep learning vs machine learning: how they scale with the amount of data

Secondly, while for producing results, Deep learning models don’t require a lot of computing power, they need a lot of it while they are trained. To solve this problem cost-efficiently,  businesses can use Cloud TPU (The Tensor Processing Unit ) that is offered as a service, charged per hour, and was created by Google specifically for Deep learning workloads. TPU is a custom ASIC chip that powers several of Google's major products including Translate, Photos, Search Assistant and Gmail. Its main benefit is that it's less than 1/5th of non -TPU processor cost.

What do you need for an effective Machine Learning/Deep Learning project?

At N-iX, we have identified seven common traits of a successful enterprise R&D project in machine learning/deep learning. Here they are:

  1. A clear objective. Before collecting the data, you need at least some general understanding of the results you want to achieve with AI and machine learning. At the early stages of the project, data scientists will help you turn that idea into actual KPIs.

  2. Robust architecture design of the machine learning solution. You need an experienced software architect to execute this task.

  3. If your business deals with big data, an appropriate big data engineering ecosystem (based on Apache Hadoop or Spark) is a must-have. It allows us to collect, integrate, store, and process huge amounts of data from numerous siloed data sources. A big data architect and big data engineers are responsible for constructing the ecosystem.

  4. Running ETL procedures (extract, transform, and load) on the newly created ecosystem. A big data architect or a machine learning engineer performs this task.

  5. The final data preparation. Besides data transformation and technical clean-up, data scientists may need to refine the data further to make it suitable for a specific business case.

  6. Applying appropriate algorithms, creating models based on these algorithms, fine-tuning models, and retraining models with new data. Data scientists and machine learning engineers perform these tasks. TensorFlow, PyTorch, Keras, Caffe are some of the popular tools which are being widely used to carry out deep learning algorithms. TensorFlow is one of the best deep learning frameworks.

  7. Lucid visualization of the insights. Business intelligence specialists are responsible for that. Besides, you may need front-end developers to create dashboards with easy-to-use UI.

Summary 

Many companies leverage Machine Learning and deep learning to derive insights from vast amounts of data, enable intelligent automation, predictive analytics, optimize operations, reduce risks and grow profits. Deep learning is typically used for solving more complex tasks and deriving insights from vast amounts of unstructured data ( texts, videos, images, sensor data). It powers such techniques of Machine learning as computer vision, speech recognition, natural language processing, and more. And it is worth using if your business generates an ongoing stream of huge amounts of data.

If you have any questions on how to launch your Machine Learning/Deep Learning project, please contact our experts.


 

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By Ivan Hetman April 07, 2020

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N-iX is an Eastern European provider of software development services with 1000+ expert software engineers onboard that power innovative technology businesses. Since 2002 we have formed strategic partnerships with a variety of global industry leaders including OpenText, Novell, Lebara, Currencycloud and over 50 other medium and large-scale businesses. With delivery centers in Ukraine, Poland, Bulgaria, and Belarus, we deliver excellence in software engineering and deep expertise in a range of verticals including finance, healthcare, hospitality, telecom, energy and enterprise content management helping our clients to innovate and implement technology transformations.

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