Discover two exciting artificial intelligence techniques.
There are many buzzwords: machine learning, deep learning, etc. Although they sound similar, they differ considerably. We’re here to define them, talk about their similarities and differences, and make sure you’ve mastered the topic by the end of this article.
Before we get there, it’s important to understand that both machine learning and deep learning are two approaches to creating artificial intelligence.
What is machine learning?
What is our definition of machine learning? “Machine learning” is a relatively broad term that encompasses methods designed to create artificial intelligence from data . Machine learning algorithms typically fall into two categories: supervised learning and unsupervised learning . In both cases, large amounts of data are used to train models to extract meaningful patterns and relationships. Trained models can be applied to new data or tasks.
Machine learning encompasses several algorithms and techniques, including a subset of techniques called “deep learning.” In this article, and as is the case colloquially, we will consider that machine learning and deep learning are two different approaches to training models to make predictions and/or decisions based on data.
Examples
Machine learning is used much more frequently than you imagine. It used to be a niche competency, but with the advent of powerful tools like PyTorch and TensorFlow, many more custom software development company have started implementing these techniques in their projects. We show you some examples of how it is used to improve our lives:
- Spam Detection – Machine learning models analyze email content to classify it as spam or non-spam. This helps filter out unwanted messages and keep your inbox clear.
- Recommendation systems : do you use social networks? Do you use any streaming applications? If so, there are machine learning models that use your preferences and behaviors to give you better recommendations.
- Medical Diagnosis – Doctors now have access to tools to enter symptoms, test results, and images to help diagnose diseases. How are you staying?
- Fraud detection: Banks use machine learning models to constantly analyze user accounts and transactions for unusual behavior to prevent fraud such as identity theft or credit card scams.
What is deep learning?
Deep learning and neural networks go hand in hand. A kind of machine learning known as “deep learning” makes use of “artificial neural networks” as a tool for data-driven learning. Neural networks use multiple interconnected layers of artificial neurons (continuing the brain metaphor) or trained nodes to synthesize higher-level patterns in the data. Deep learning is considered a more powerful method of machine learning as it can extract meaningful information from larger data sets and solve more complex problems. Consequently, training deep learning models requires significantly greater computational power and capabilities.
Real examples of deep learning
We use software development services such as deep learning when the problem is more complex and requires more training and processing than the standard machine learning technique is capable of. By “complex” we do not mean something like traveling to space: things as simple for humans as recognizing shapes and objects is an extremely difficult task for machines. Let’s look at an example.
Suppose you are training a machine learning model and a deep learning model to recognize a house. A machine learning model might consider a large, rectangular structure on the ground to be a house. However, a deep learning model would define houses as structures with windows, doors, and a roof. If you provide them with a photo of an upside-down house, the machine learning model may not be able to deduce it due to the position of the ground and sky. For its part, the deep learning algorithm would recognize the windows, doors and roof and, therefore, correctly conclude that the object in the photo is really a house.
- Natural Language Processing – Have you heard of ChatGPT? Languages are incredibly rich and complex, but deep learning models are able to consume and synthesize language like no other technique to date.
- Autonomous driving : the future is here! Many automakers (not just Tesla) already offer self-driving software. Under the right conditions, you can let go of the steering wheel and let the car take you to your destination. Autonomous driving models will only improve and it is expected that all cars will incorporate them as standard soon.
- Drug discovery : Science is never easy, so pharmaceutical companies employ deep learning models that analyze large chemical data sets to identify potential drug candidates and accelerate their discovery process.
How do I become an expert?
Becoming an expert in machine learning or deep learning requires a combination of experience, knowledge, curiosity, and skills. No degree is necessary, but training through a bootcamp or some academic course is definitely useful to develop solid knowledge bases that will help us take the leap. These are the steps we recommend to become an expert in machine learning:
- Develop a strong mathematical foundation – Machine learning is a branch of computer science and as such requires a strong mathematical foundation. You don’t need a degree in mathematics to get started, but you should be comfortable with topics such as linear algebra, calculus, probability, and statistics. If this is not your case yet, don’t worry: you can still get started in the world of machine learning and learn the theory as you go.
- Learn to program: Although machine learning is theoretical, it is a fully applied discipline. The best way to put machine learning or deep learning into practice is with Python. The vast majority of the programming community uses Python and libraries such as Pandas, scikit-learn, TensorFlow and PyTorch.
- Practice your skills : practicing is essential. There are many free datasets publicly available that you can use to put machine learning techniques into practice. Practical experience helps you understand knowledge better and develop more problem-solving skills.
- Be active in the community : to be considered an expert, you have to be up to date with the latest trends and news. Staying in touch with professionals in the field, attending industry events, and reading academic articles are ways to stay up to date.
- Bet on advanced topics : When you have a solid foundation, you can explore advanced topics such as deep learning, reinforcement learning, computer vision or natural language processing. Focus on what interests you most.
Which is better? Machine learning or deep learning
None is better or worse . The pros and cons of each approach depend largely on the set of problems you want to solve. In general, machine learning is a good choice for problems that can be solved with relatively simple algorithms, while deep learning is better suited for problems that require more complex analysis and can benefit from the power of neural networks. Let’s look at examples of use in which machine learning is more recommended than deep learning and vice versa.
Machine learning is better if…
- The data set is small : Machine learning algorithms can train models with much less data than you imagine.
- The results must be interpreted : deep learning models are usually black boxes; If you need to understand the results of the model, a machine learning approach will be more advisable.
- You need to make predictions quickly : Training machine learning models is much faster, as they require less data and less processing power.
Deep learning is better if…
- The data set is large : Deep learning algorithms require large amounts of data.
- You need to make more accurate predictions : We are not claiming that machine learning models are not accurate; however, a deep learning model may be more advisable.
- There is a complex problem to solve : Some traditional machine learning algorithms may have difficulty finding patterns in complex data sets. In this case, opt for a deep learning model.
In the end, the choice of one technique or another depends on the specific problem you are trying to solve. If you are not sure which one would be best for you, you can consult with a data scientist or machine learning expert.
For all the innovations we’re seeing across a wide range of industries, machine learning is probably the most exciting field today. If you want to be at the forefront of innovation, unleash the full potential of data, and have a meaningful impact on the world, the best starting point is to enroll in one of Ironhack’s bootcamps. Enjoy the journey, take advantage of the opportunity and acquire the skills and knowledge necessary to enter the universe of machine learning. What are you waiting for?
About the Author:
Glad you are reading this. I’m Yokesh Shankar, the COO at Sparkout Tech, one of the primary founders of a highly creative space. I’m more associated with digital transformation solutions for global issues. Nurturing in Fintech, Supply chain, AR VR solutions, Real estate, and other sectors vitalizing new-age technology, I see this space as a forum to share and seek information. Writing and reading give me more clarity about what I need.
