Most people don't realize that machine learning, a type of artificial intelligence (AI), was born in the 1950s. Arthur Samuel wrote his first computer learning program in 1959. In this program, the IBM computer got better and played with the Checker game. Fast forward to today where AI is not cutting edge technology. It can lead to highly paid and exciting jobs. Machine learning engineers are in high demand. Because, as Upsaily Mle Tomasz Dudek says, neither data scientists nor software engineers have the exact skills needed in the field of machine learning. Companies need experts who are proficient in both these fields, but they can do what neither data scientists nor software engineers can do. That person is a machine learning engineer.
The terms “artificial intelligence,” “machine learning,” and “deep learning” are often thrown interchangeably, but if you're considering a career in AI, it's important to know how they differ. According to Oxford's Living Dictionary, artificial intelligence is “the theory and development of computer systems that can perform tasks that normally require human intelligence, such as visual recognition, speech recognition, decision-making, and inter-language translation.” They may be called “smart,” but some AI computer systems do not learn on their own. That's where machine learning and deep learning come.
What is machine learning?
Machine learning allows computer systems to learn from data entered without being continually reprogrammed. In other words, they continuously improve performance on tasks without the additional help from humans. For example, play a game. Machine learning is used in a wide range of fields, including arts, science, finance, and healthcare. There are also various ways to train your machine. Some are simple, such as basic decision trees, while others are much more complex, including multiple layers of an artificial neural network. The latter occurs in deep learning. We'll get to that soon.
More information: Machine Learning Tutorial: A Step-by-Step Guide for Beginners
Machine learning not only used Arthur Samuel's groundbreaking program in 1959, but also the Internet was continuously improved with Checkers, using a relatively simple (by today's standard) search tree as the main driver, but the Internet was also improved as well. Thanks to the Internet, a huge amount of data is created and stored, and can help “learning” it to make it available to computer systems.
Machine learning with R and machine learning with Python are two common methods used today. Although we won't discuss specific programming languages in this article, it's helpful to know if you want to dig deeper into R or Python and deeper into machine learning using R or Python.
What is deep learning?
We consider deep learning to be the next frontier, cutting edge, machine learning. You may already be experiencing the results of a detailed, deep learning program without realizing it! If you've watched Netflix, you've probably seen recommendations on what to watch. Additionally, some streaming music services select songs based on what you've heard in the past, or songs you've given a thumbs up or pressed on the Like button. Both of these features are based on deep learning. Google's voice recognition and image recognition algorithms also use deep learning.
Just as machine learning is considered a type of AI, deep learning is often considered a type of machine learning. It is called a subset. Machine learning uses simpler concepts such as predictive models, while deep learning uses artificial neural networks designed to mimic human thinking and how to learn. From high school biology, you may remember that the main cellular components and main computational elements of the human brain are neurons, and each neural connection is like a small computer. The network of neurons in the brain is responsible for processing all kinds of inputs, such as vision and sensations.
With deep learning computer systems, like machine learning, inputs are still fed to them, but information is often in the form of a huge dataset, as deep learning systems require a large amount of data to understand it and return accurate results. Next, artificial neural networks ask a series of binary true/false questions based on data containing highly complex mathematical calculations and classify the data based on received responses.
So, both machines and deep learning fall into the general classification of artificial intelligence and “learning” from data input, but there are some important differences between machine learning and deep learning.
Incidentally, if you want to know more about deep learning in detail, you can check out this introduction to the deep learning tutorial. Tensorflow is one of the most popular libraries for implementing deep learning, so it is worth learning individually about deep learning with Tensorflow.
5. Important differences between machine learning and deep learning
1. Human intervention
In machine learning systems, humans need to identify and manually code the applied features based on data types (e.g. pixel values, shapes, orientations, etc.), but deep learning systems try to learn those features without additional human intervention. Take the case of a facial recognition program. This program first learns to detect and recognize face edges and lines, then recognize more important parts of the face, and finally the overall representation of the face. The amount of data involved in doing this is enormous, and as time passes and the program trains, the probability of correct answer (i.e. accurately identifying faces) increases. And that training is done through the use of neural networks, just like how the human brain works, without the need for humans to replicate the program.
2. Hardware
Due to the amount of data being processed and the complexity of mathematical calculations contained in the algorithms used, deep learning systems require much more powerful hardware than simpler machine learning systems. One type of hardware used for deep learning is the graphical processing unit (GPU). Machine learning programs can be run on low-end machines without increasing computing power.
3. time
As you can imagine, deep learning systems can take a lot of time to train because of the huge datasets needed by deep learning systems, and because so many parameters and complex mathematical formulas are involved. Machine learning can take seconds to hours, while deep learning can take hours to weeks.
4. approach
The algorithms used in machine learning tend to analyze data in parts, and combine those parts to come up with results or solutions. Deep learning systems look at the whole problem and scenario at once. For example, if you need a program to identify specific objects in an image (what they are, where they are, for example, a car's resense plate in a car in a parking lot), you need to perform two steps: first object detection and object recognition. On the other hand, when you use a deep learning program, when you enter an image and train it, the program returns both the identified object and its location to one result.
5. application
Given all the other differences above, you probably already understand that machine learning and deep learning systems are used in a variety of applications. Where they are used: Basic machine learning applications include forecasting programs (such as predicting stock market prices or where the next hurricane hits), emails for Spam identifiers, and programs that design treatment plans based on evidence of medical patients. In addition to the above examples of Netflix, music-riding services, and facial recognition, one of the highly publicized applications of deep learning is self-driving cars. The program uses many layers of neural networks to determine which objects to know whether to avoid traffic lights or speed up or slow down.
Read more: Top 10 Machine Learning Applications
Future trends in machine learning and deep learning
The possibilities for machine learning and deep learning in the future are almost limitless! The increased use of robots is no surprise in ways that can improve daily life in major and minor ways, not just manufacturing. Deep learning can also change the healthcare industry as it helps physicians do what they previously attempt to predict or detect cancer. On the financial side, machine learning and deep learning are poised to help businesses and individuals save money, invest more wisely, and allocate resources more efficiently. And these three areas are just the beginning of future trends in machine learning and deep learning. Many areas of improvement are now merely sparks of the imagination of developers.
Conclusion
Hopefully, this machine learning vs deep learning article offers all the basics about machine learning and deep learning, giving you a glimpse into the future trends of machine learning and deep learning. As you may have understood, becoming a machine learning engineer is an exciting (and profitable!) time. In fact, according to PayScale, Machine Learning Engineers (MLEs) pay ranges are $100,000-$166,000. Therefore, there was no good time to start studying or deepen your knowledge base to be in this field. If you'd like to take part in this cutting-edge technology, check out Simplilearn's Deep Learning course. Also, if you need credentials to boost your resume to promote your career in AI, Professional Certificates for AI and Machine Learning Collaboration with IBM.
You can also work with IBM to employ the Artificial Intelligence Engineer Master Program. The program provides detailed knowledge about Python, deep learning with tensorflow, natural language processing, speech recognition, computer vision, and reinforcement learning.
