What exactly is Machine learning and Deep learning?
Machine learning is a subset of AI that focuses on the design of a system. It can learn and make decisions or predictions based on the experience which is data in case of machines.
It enables a computer to act and make data-driven decisions rather than being explicitly programmed to carry out a specific task.
These programs are designed to learn and improve over time when exposed to new data.
For example, while shopping online and checking for a product, you must have come across or would have noticed a line saying “the people who bought this, also bought…” giving you recommendations.
Moreover, have you ever noticed that it also suggests for a product similar to what you’re looking for? How are they able to do this? The answer is Machine Learning.
Deep learning is a subset or an exciting branch of machine learning (ML) that uses similar ML algorithms and uses lots of data to educate deep neural networks so, as to attain better accuracy.
DL, with Artificial Intelligence, is uncovering hidden techniques and opportunities in the field of healthcare, helps doctors in surgical complications, drug development, patients, and record mining. It furthermore gives better assistance in voice search and image recognition.
Nowadays, the Voice search tool is in nearly every smartphone. Google Now, Apple’s Siri, Microsoft Cortana are some applications of voice-activated assistance which run on deep learning.
Let’s review the differences between the two:
Machine learning uses algorithms to analyze data, then they learn from that data and make informed decisions on the basis of what it has learned.
Whereas, deep learning learns through an artificial neural network which is why it is considered as more human-like. It doesn’t require a human programmer to tell them what to do, they learn and make confident decisions on its own.
With that been said let’s see how Machine and Deep Learning work?
Machine Learning uses a type of automated algorithm that acquires a piece of knowledge to predict future decisions using the data fed to it. The analysts direct a variety of these algorithms to inspect the distinct variables in the data set.
So basically, there are three types of learning algorithms:
Supervised ML Algorithms: It makes predictions. Afterward, these algorithms search for patterns within the value labels, allotted to the data points.
Unsupervised ML Algorithms: Labels do not correlate with data points. Also, these machine learning algorithms classify the data into a group of clusters. Furthermore, it describes and makes complex data look simple and classified for analysis.
Reinforcement ML Algorithms: These algorithms employ to choose an action, based on respective data points. This algorithm can furthermore continue to change its plan of action to learn better.
Nonetheless, a deep learning model parses data with a structure similar to how a human would figure out conclusions.
To attain that, deep learning uses an arrangement of algorithms known as an “artificial neural network”.
The artificial neural network (ANN) mimics the biological neural network of the human brain and gets inspiration from the function and structure of a human brain.
Besides, there are numerous layers to process features namely the input layer, the output Layer, and the Hidden Layer.
Also, typically, each layer extracts a chunk of valuable information. For instance, one neural net processes pictures for steering a self-driving car then each and every layer would process something unique.
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