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Technologies have become more integrated into our day-to-day operations, and organizations are heavily relying on deep learning and machine learning algorithms to keep up with the pace of consumer demands. You can witness its application in social media via object recognition in photographs or directly communicating with devices like Siri or Alexa.
Machine learning and deep learning concepts have seen ground-breaking developments in artificial intelligence (AI) during the last decade, reshaping the world around us. Businesses are enthralled by the potential opportunities to invest in these emerging technologies, from AI to blockchain. However, Deep Learning and Machine Learning are artificial intelligence terms, and their definitions may appear interchangeable to newcomers.
Deep learning and machine learning have recently seen a golden era as these applications have become ubiquitous. In addition, these technologies have become increasingly personalized in our interactions with them. For instance, chatbots can be annoying and still pretty clumsy in their current state, but they are evolving. They have become conversational, learning human characteristics, increasing user experience, and supplementing your customer service approach.
According to the Comet survey, the cloud-based meta-machine learning platform, 508 ML practitioners stated that 58 percent of respondents use manual tools to track ML experiments, while 47 percent said that ML teams require 6 months to deploy a single ML project. Moreover, 88 percent of respondents have a budget of less than USD 75,000 for ML tools and infrastructure.
Echoing similar sentiments for deep learning are other surveys. Grand View Research predicted that global deep learning industry market size is estimated to grow USD 526.7 billion at a CGAR of of 31.8% from 2025 to 2030.
Your organization can decide which one to implement based on the business requirements. Let’s explore the concepts before diving into the topic’s crux.
Useful Link: 10 Ways Artificial intelligence (AI) is Transforming DevOps
What is Deep Learning (DL)?
The term deep learning was coined 79 years ago. In 1943, mathematician Walter Pitts and neurophysiologist Warren McCulloch worked together to create a computer-based model. They then began translating neural networks into computational systems of the human brain.
Deep learning is a subset of the machine learning family and is considered artificial intelligence. It is a powerful tool that uses three or more layers of neural network algorithms to perform sophisticated computations on massive data.
It is the most crucial element of data science, including predictive modeling and statistics. DL makes the process easier and faster for data scientists and researchers, who interpret, analyze, and collect a considerable volume of data.
The Deep learning model has attracted considerable attention because it drives many AI services and applications, such as high computing power, improved automation, advancements in data center capabilities, and the ability to perform analytical and physical tasks without human intervention. In addition, deep learning algorithms perform routine and repetitive tasks more effectively and quickly than humans.
Deep learning can benefit real-world applications such as voice-enabled TV remotes, digital assistants, credit card fraud detections, and emerging technology such as self-driving cars.
In deep learning, models operate different layers to discover and learn insights from the data. Some of the best deep learning models are:
- Recurrent Neural Network
- Convolutional Neural Network
- Autoencoders
- Classic Neural Networks and more
How Does Deep Learning Work?
Let’s start with the algorithm’s working principle before moving to the deep learning model. For instance, we all know about neurons in the brain (cells) that capture inputs coming through sensory organs like ears, eyes, etc. When a neuron receives an input signal, it passes through the body and gets activated to produce output. This output will stimulate other neurons to activate to get the complete picture.
Understanding how deep learning works with the above instance, the deep learning model takes the images as input and feeds them directly to the algorithms without any manual feature extraction step. The input passes to multiple layers of the AI neural network to estimate the final output.
What is Machine Learning (ML)?
Arthur Samuel coined the term machine learning in 1959 and is a pioneer of artificial intelligence and computer gaming. Over the past few years, the top MNC companies have invested significantly in this technology and left no stone unturned to stay on top of today’s IT world.
Machine learning (ML) is a branch of artificial intelligence that enables systems to adapt to new data independently and through iterations. It is a process of data analytics that automatically accesses data and performs tasks through detections and predictions. ML focuses on developing computer programs that can learn data, make decisions, and identify patterns, which involves minimal human intervention.
Machine learning is a member of the artificial intelligence family that aims to create software that can automatically learn from past data to gain knowledge and gradually improve its learning behavior to make the correct predictions depending on the new data.
ML is one of the most pivotal and commanding technologies that one would have ever encountered. Yet, despite the scope of improvement and investment, developing, maintaining, and training ML models has been ad hoc and cumbersome. This is where MLOps solutions come into play, offering a streamlined approach to manage and operationalize machine learning models. MLOps solutions automate and optimize ML models’ deployment, monitoring, and management, reducing the friction in turning machine learning into impactful business solutions.
Machine learning is a crucial concept for any aspiring data scientist or analyst and those who wish to transform a large amount of raw data into predictions. Deep learning and machine learning fall under artificial intelligence; both models learn from data input.
How Does Machine Learning Work?
Machine learning primarily involves two different approaches: supervised learning and unsupervised learning.
Useful Link: Top 15 AWS Machine Learning Tools in the Cloud
A) Supervised Learning
Supervised learning is an algorithm that learns from labeled training data to help machines predict the output. It deals with the unlabelled data. It is a subcategory of machine learning and artificial intelligence.
B) Unsupervised Learning
Unsupervised learning uses machine learning algorithms to cluster and analyze hidden patterns in input data without regard to output. It allows users to perform more complicated tasks compared to supervised learning. Unsupervised learning algorithms include neural networks, clustering, anomaly detection, and more.
Comparison Between Deep Learning and Machine Learning
Let’s explore the differences between deep learning and machine learning on different parameters.
| Details | Deep Learning | Machine Learning |
| Model | It is a specialized subset of machine learning. While it comes under the broad category of artificial intelligence | It is a subset of artificial intelligence and a superset of deep learning |
| Data dependency | DL algorithms need massive data, and users must feed that vast amount for better performance. | Even though ML depends on a vast volume of data, it can also manage a smaller amount of data. |
| Data representation | Deep learning data differs entirely from machine learning, which runs neural networks. | Data performed in machine learning is wholly different from deep learning in that it runs structured data. |
| Number of data points | Deep learning can use thousands of data points to make predictions | Machine learning can use millions of data points to make predictions |
| Execution time | Deep learning usually takes a long execution time to train the model but less time to test it. | ML algorithm comparatively takes less time to train the model but takes a long time to test the model. |
| Accuracy | It enables faster and more accurate results | It has low accuracy results |
| Performance | It shows better performance on massive datasets | It shows good performance on small and medium-size datasets |
| Hardware dependencies | It works on high-end machines | It works on low-end machines |
| Featurization Process | It requires high-level features from data and develops new features by itself. | Needs features to be identified accurately and created by users |
| Output | DL output is usually a numeric value like a score or a classification | ML output has numerous formats, such as score, sound, or text |
| Problem-solving approach | The DL model’s problem-solving approach is entirely different from the traditional ML model. It takes input and produces the result, following an end-to-end approach. | The traditional ML model breaks the problem into subfields, and after solving each product, it gives the final result. |
| Introduced | The term deep learning was coined by mathematician Walter Pitts and neurophysiologist Warren McCulloch in 1943 | Arthur Samuel coined the term machine learning in 1959 |
| Interpretation of result | When working with a deep learning model, interpreting the result for a given problem is complex. | When working with a machine learning model, interpreting the result for a given problem is easy. |
| Feature Engineering | In deep learning, there is no need for feature engineering as the neural network automatically detects essential features. | In machine learning, feature engineering is done by humans |
| Type of data | The deep learning model mainly works with structured and unstructured data | Machine learning models work with a structured form of data |
| Suitable for | It is suitable for solving complex problems | It is suitable for solving simple problems |
| Human Intervention | The deep learning model permits learning features without additional human involvement. | In the machine learning model, humans must identify and hand code the applied features depending on different data types, such as orientation, shape, pixel value, etc. |
| Applications | DL is used in applications such as healthcare, self-driving cars, advanced video game AI, biometrics, and search engines like image search and text search. | ML is used in data analytics, facial recognition, fraud detection, vehicle number plate identification, customer service, finance and banking, and manufacturing applications. |
| Training | It trains on Graphics Processing Unit (GPU) for proper training | It trains on the Central Processing Unit (CPU) for proper training |
| Tuning capability | It can be tuned in numerous ways | The ML model has limited tuning capability for hyperparameter |
| Training dataset | Huge volume | Small volume |
| Choose features | No | Yes |
| Number of algorithms | Few | Many |
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Conclusion on Deep Learning and Machine Learning
Understanding the latest advances in artificial intelligence (AI) might be daunting. Still, if you only want to master the basics, many AI discoveries can be boiled down to two concepts: deep learning and machine learning. There are numerous examples of deep learning and machine learning, and we can witness them everywhere.
How self-driving cars become a reality, how Netflix predicts which show you’ll like to watch next, and how Facebook detects who is in a photograph. These models seem interchangeable buzzwords in the artificial intelligence industry but have some key differences.
As a result, deep learning is the way to do business if you have enormous data and powerful technology. Otherwise, implement a machine learning model for your business. As you can witness numerous benefits while opting for both these models, picking the best technology is an arduous task that makes it challenging.
Therefore, instead of one model, why not implement the deep learning and machine learning technologies that benefit your business? However, adopting both these technologies is more difficult said than done, which is one of the many reasons enterprises seek Veritis’ assistance.
Are you looking forward to implementing deep learning or a machine learning model in your business? Then Veritis, the Stevie Awards winner, is the right choice to offer digital transformation services for your business. Veritis, the trusted tech partner for Fortune 500 organizations and emerging companies, provides cost-effective solutions without compromising quality alongside expert MLOps services to streamline your AI model deployment and operations.
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Additional Resources:
- All You Need to Know about Artificial Intelligence as a Service (AIaaS)
- What is MLOps? Why MLOps and How to Implement It
- How AI Adoption Will Transform Your Business
- ITOps vs DevOps vs NoOps Comparison
- AWS vs Azure vs GCP: Cloud Cost Comparison
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