20 Oct · 6 min read
You've probably seen the terms artificial intelligence (AI), machine learning, and deep learning used recently, even if you're not active in the field of data science. They are even utilized interchangeably at times. While connected, each of these words has a unique significance and goes beyond simply being catchphrases. In this blog, we will be specifically talking about Machine Learning and Deep Learning and how they differ.
Machine learning can be understood as computers learning from data itself. It speaks of the nexus between statistics and computer science, where algorithms are utilized to carry out certain tasks without being explicitly coded; instead, they identify patterns in the data and generate predictions as soon as fresh data is received.
Machine learning has the ability to speak to text. Specific computer programs that can convert both live and recorded speech into text files. The speech can also be segmented into time-frequency bands based on intensities.
Image recognition is a well-known and frequent use of machine learning in the real world. It can identify an object as a digital image in color or black and white photographs based on the brightness of the pixels.
Machine learning can help with disease diagnosis. Many medical professionals use chatbots with speech recognition capabilities to spot symptom patterns.
Machine learning can classify available data into groupings that are further defined by rules created by analysts. Once classification is complete, analysts can determine the fault.
Deep learning algorithms can improve their results through repetition without human interaction, but machine learning algorithms typically require human correction when they make a mistake. A deep learning algorithm needs vast data sets, which may include heterogeneous and unstructured material, in contrast to a machine learning algorithm, which can learn from relatively small amounts of data.
Deep learning is sometimes referred to as the cutting edge when it comes to machine learning. Without you realizing it, you may already have encountered the outcomes of a comprehensive deep learning program! You've probably seen Netflix's suggestions for shows to watch if you've ever used the service. Additionally, some streaming music services select tracks based on what you've already listened to or songs you've liked by clicking the "like" button or giving them a thumbs up.
Deep learning is frequently seen as a more advanced version of machine learning; others refer to it as a subset, much as machine learning is regarded in terms of AI.
Despite the fact that automatic machine translation is nothing new, deep learning is improving it by utilizing layered networks of neural networks and enabling translations from images.
Machines with deep learning are starting to distinguish between dialects of a language. When a machine determines that someone is speaking English, it activates an AI that is trained to identify dialect distinctions. Once the dialect is identified, another AI who is knowledgeable about that dialect will take over. There is no human involvement in any of this.
As an autonomous car travels along the street, multiple AI models are at work. While some deep learning models are trained to recognize pedestrians, others are designed to specialize in street signs. Up to one million unique AI models can provide information to a car as it travels along the road, enabling the car to take action.
For picture classification, object recognition, image restoration, and image segmentation, deep learning has produced super-human accuracy; even handwritten digits may be detected. Massive neural networks are used in deep learning to educate machines on how to automate operations that are currently done by human visual systems.
Due to the volume of data processed and the complexity of the mathematical computations required by the algorithms utilized, deep learning systems require technology that is significantly more powerful than machine learning systems. Among the types of hardware utilized for deep learning are graphics processing units (GPUs). Machine learning applications can run on less powerful devices with fewer processing resources.
In contrast to machine learning systems, which require a human to manually define and code the applied characteristics based on the data type, a deep learning system seeks to learn such features without further human input (for example, pixel value, shape, and orientation). Take a look at a facial recognition software program. The algorithm first learns to detect and recognize a face's borders and lines, then its more significant characteristics, and finally its general appearance.
Data is commonly divided into sections by machine learning algorithms, which are then combined to provide a result or solution. Deep learning systems approach a situation or a problem in it entirely. For instance, you would need to use machine learning to go through two steps: object detection and object recognition, if you wanted a program to identify certain items in an image (what they are and where they are—for example, license plates on cars in a parking lot). Contrarily, if you used deep learning software, you would input the image, and after analyzing, the algorithm would provide a single output that included both the location of the recognized items in the image and their identification.
As you might think, it can take a long time to train a deep learning system because of the enormous amounts of data that are needed, the number of parameters, and the intricate mathematical formulas involved. Deep learning can take a few hours to a few weeks, whereas machine learning can be completed in as little as a few seconds to a few hours! In short, machine learning wins here!
You have probably already realized that machine learning and deep learning systems are employed for various purposes given all the other differences described above. Where to use them: Predictive programs, algorithms that provide evidence-based treatment regimens for patients, and email spam detectors are examples of basic machine learning applications (such as for predicting stock market price movements. One widely publicized use of deep learning is in self-driving cars, which use multiple layers of neural networks to perform tasks like identifying objects to avoid, recognizing traffic lights, and determining when to speed up or slow down. Other examples include Netflix, music streaming services, and facial recognition.
So hopefully this post on machine learning & deep learning has covered all the fundamentals and given you a glance at where these fields are headed in the future. Both of them play an important role in different fields and are going to be unmissed technologies in the coming future. If we missed something about machine & deep learning, please leave your thoughts in the comments below!