Machine learning and deep learning are two different and major families of Artificial Intelligence grabbing many people’s attention over the years, but we are going to give you a deep difference between these two buzzy words. If you are interested to make a massive difference, then you are in the right place and there is no better place than us.
As you know the data is increasing day by day through different mediums like google and social media marketing etc. and it is very important to extract useful information from all these unsupervised data. Machine learning and deep learning are the names that play with all the data and play a very useful and positive role in the lives of humanity.
Deep learning vs machine learning
Machine learning itself a famous and successful algorithm of AI and plating a viral role over the years but the simple difference is that deep learning is also a machine learning expect the fact that we deal with perceptron in deep learning which is basically inspired by the neuron of the brain.
In machine learning, the modal is trained on the data and make a decision based on the presented data of different institution. For example, you may consider an example of the music playlist of a single man and model name is music stream service. Forgiving the service of the best song the ML algorithm will take decisions from the other same interesting list of songs and then recommend some interesting song to the user. This is basically the idea of the AI which is used by many companies and making decisions is all upon the same algorithm which is a subset of machine learning and this process is called an automated recommendation.
This machine learning automated recommendation is mostly used by security firms (malware detection) to the business professional (artificial business), therefore, any organization can make a massive list of all the circumstances due to which they are not getting.
Machine Learning is a combination of complex mathematics, statistics, and coding as well which is exactly working or manipulating as a flashlight, a car, or a computer machine that is working on the coded and planned data. When someone talks about the working of machine learning for some program or model then it means that the machine can work and process according to the data and improve the performance with the passage of time at the end of a best and successful model.
For example, when you introduced a device like the light of a smartphone with the keyword dark then the light will turn on whenever you say a word dark and it will automatically turn on the light in turn which means the device is working using machine learning.
Now in deep learning is another terminology where the features are automatically extracted which means there is not any defined method to feature engineering so we can say that a neural network is a black box where you input some items and the output are obtained at the other end.
When talking about the idea of deep learning then this is machine learning with different priorities but deep learning has most demanded so we can say that if you really want a big result then you need more data and more hardware resources to complete the process.
Working with classical machine learning is based on the instruction given by the data scientist which means he will make a list of all the features and then feed it to the model-based one the desired results. The more the number of features the more the complex model and more concentration and coding is required so machine learning is totally different and deep learning is required more amount of data.
Let us take an example of image classification and it is a binary class classification for two classes. There are different algorithms of binary classification using machine learning and deep learning, so deep learning is more demanded because of its amazing and fruitful results. The most important two things about deep learning are a massive amount of data and more amount of hardware resources. Deep learning will give good results when the number of data increases. While on the other hand when the number of data increases then more amount of hardware resources is required for data processing. Another important and most liked about it is feature engineering. Deep learning is a just like machine where the input is given at one end the output can get on the other end. Moreover, if you want to get more desired and amazing results then you must get more amount of data.
There are two phases one is training is other testing and then the process of delivering the final product begins. Machine learning takes less amount of time for training while a lot of time is required for testing because we give features to the model at training time and less time required for training purposes. On the other hand, the testing phase is much time taking because of matching the trained feature to the testing.
Let us talk about deep learning. It takes much time for training and less time for testing. Now the reason is much simple because deep learning extracts all the features itself and it is time-taking as it leads to the feature extraction and less time for training because it is already trained on all the features. When all the features are extracted and learned easily then it will take much less time for testing.
Working of Deep Learning
An artificial neural network (ANN) is the gate through which deep learning works like a human and mimics human work. Deep learning uses a layered structure where each layer consists of perceptron and perceptron is basically inspired by the neuron of the brain. A brain uses natural intelligence for some work but deep learning uses a different algorithm for forwarding propagation and backward propagation as well.
The design of an artificial neural network is inspired by the biological neural network of the human brain, leading to a process of learning that’s far more capable than that of standard machine learning models.
The design of ANN is made and inspires by the brain setup. Let us dive into it and let us consider first the working of the brain. The brain is made of neurons and these neurons send message to other using dendrites.
While deep learning uses perceptron who use activation function to fire the final output.