Basics of Machine Learning, Deep learning or Neural Networks:
Machine learning:
Machine Learning is a great part of the Artificial intelligence field and is a popular way to execute AI projects through different methods. Normally in ML, there is no need to instruct computers, data is given to the system and according to prior experience and data, rules are made to apply further.
In the Machine Learning domain, models are trained on learning how to create rules for themselves to perform lookalike tasks by taking specific inputs by their desired possible outputs. ML typically requires processed or organized data with the involvement of humans.
What is Deep Learning
Study of neural networks is called deep learning. DL is the study of doing ML with neural networks. In 2012, Neural Networks made up of basic and fundamental mathematical functions like addition, subtraction, multiplication and division etc. these networks are like neurons in the human brain to make a digital brain.
Deep Learning is a subset of Machine Learning which is based on Neural Networks. Multiple Layers exist in deep learning in order to extract data.
First Neural Network was introduced by Marvin Minsky who was an American cognitive and computer scientist in the 1960s, but this proposal failed due to hype created in that era and other factors.
Why did the idea of Neural Network fail in the 1960s?
The idea of Marvin Minsky was not considered true due to hype and due to lack of computations and training. Because his network was based on simple statistical computations and had no approach to complex calculations.
Which factors are needed to make Neural Networks?
Three factors are required for making neural networks:
Computational power
Bulk of Data
Learning new Algorithm
What is the most popular digital neuron in history?
Most Popular neuron in the history of Deep Learning was ReLU(rectified linear unit) . The function of ReLU is to provide greater numbers from two different numbers. Basically, ReLU is called an activation function and it deals with the MAX function.
What is a network?
Two are more things when interlink together in a sequence for output is called Network.
Types of Network:
As we know that multiple layers are there in Deep Learning in order to refine data and deep learning is the study of Neural networks. So, Neural networks are of different types and consist of many layers to do complex computations. One of them is simple neural network.
Simple Neural Network:
It consists of three layers;
Input layer: In this Layer, input is given to the system. It can be an image of an animal, text etc.
Hidden layers: This layer can be more than one depending on the complexity of the data given to the system. The more complex the data, the more the layers will be.
Output Layer: This layer gives the desired result of the data given as input.
Why is the Neural Network called Deep Learning?
Neural Networks are actually Deep Learning, the difference is just of names. This is so because, in early times, no one raised funding for the research on Neural Network due to hype, so researchers changed the name and called it Deep Learning.
Machine Learning Vs Deep Learning:
Machine Learning and Deep Learning, both use Neural Networks for performing their tasks by different approaches but there is a difference between both of them.
Machine Learning:
Machine Learning always needs human intervention for data processing.
Maximum number of Machine Learning algorithms do not respond to Unstructured data.
Conventional Machine Learning algorithms can’t deal with unlimited data.
Deep Learning:
Deep Learning accepts any type of data as input whether it is structured or unstructured.
As more hidden layers, more it will be learnt and provide refined output.
Great work. Interesting knowledge👍
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