What is Deep Learning ? Deep learning Vs Machine Learning

👉If You want to have a basic idea about deep learning and Planning of having a career in deep learning but don’t know anything about deep learning then you are in the right place. In this article you will learn about What is Deep Learning, Machine Learning Vs Deep Learning, Types Of Deep Learning, Application of deep learning and much more so read this article only if you are ready to invest your precious time into it now, If you are ready then start scrolling to explore Deep Learning Technology
👉Deep Learning is one of the branches of the Machine learning Tree which is found in the Artificial Intelligence Garden or Deep Learning is a subset of machine learning which is itself a subset of Artificial Intelligence. Deep Learning is basically a machine learning technique that learns features directly from the data. To be precise labeled data. In Deep Learning first large amount of labeled data is taken as input then the labeled data is processed and then finally useful data is given out as output that is why deep learning is sometimes called as end to end learning , For example in terms of data as images . You are provided with large amount of labeled data and you want to divide that labeled data in 4 different categories – cars images , bikes images , nature images , house images . Now what deep learning algorithm will do it will take large amount of labeled data(images) as input and then learns how to classify images into different Categories and then finally when images get categorized the data will be shown as output . Deep learning algorithm learns directly from the data that’s why it is known as end-to-end-learning . Deep Learning models enacts the functioning of human brain that’s why they are some times referred to as deep neural network.

How Deep Learning Works?

👉Deep Learning or Deep Neural Network consists of 3 layers (Input layer, hidden layer, Output Layer ). Each of these layer consist of there own weights which automatically multiplies themselves with the incoming data and are responsible for the functioning of deep neural network 
How Deep Learning Works

1.Input Layer

 The input layer is the first layer of deep neural network which receives large amount of labeled data as an input , inside the input layer data has to pass from activation function before getting passed to the first hidden layer . Input layer has its own weights which automatically multiply themselves with the incoming data before getting passed to the activation function 

2.Hidden Layer

👉It is the middle layer located between input layer and output layer . Hidden layer is responsible for the processing of labeled data which means all the computation of data is done inside hidden layer and then data is sent to output layer where data is given out as output . “Deep” in deep learning refers to “many” which means there are many hidden layers or number of hidden layers present inside a deep neural network to process the raw data into required data 

3.Ouput Layer

👉It is the last layer of deep neural network which is located just after the hidden layer. function of output layer is to receive the data from hidden Layer (After data is Processed inside Hidden Layer) and then send that data as output to the program.

Types Of deep learning or deep neural network

Types Of Deep Learning Models

Deep Learning Vs Machine Learning

1.Deep Learning requires large amount of labeled data while in machine learning large amount of labeled data is not required to predict the output
2. Deep Learning requires high-end systems to operate while machine learning can easily work on low specs machine. Deep Learning requires an additional Hardware Component which is GPU(Graphics Processing Unit) commonly known as Graphics Card to process large amount of high-quality data such as a set of HD images 
3.I takes a lot of time sometimes even week to months to train a deep learning model, on the other hand, it does not require a lot of time to train a machine learning model sometimes it takes only a few minutes to train a machine learning model.
4.Deep Learning Models are more accurate and efficient than machine learning Models because deep learning mimics the functioning of the brain to extract the data from the labeled data and then process it. Processing is done in multiple layers stack over each other  which make it more accurate and effective than traditional machine learning model 
5.Deep Learning Models learns directly from the data by extracting the features from the labeled data and then processing it on the other hand machine learning models works on algorithms which are assigned to them to perform a specific task

Deep learning Applications

Application of Deep Learning

My perspective on Deep Learning

It is one of those rapidly developing technologies which has a very great scope and career in the future. If you are thinking of getting into deep learning industre then today is the right time because there is almost no competition in this industry which means more opportunity to do something great. But most people don’t like these technologies because of a belief that they are going to take jobs but this is not true if you have enough skills and dedication towards your work then nobody can take your job from you. Every technology has there own pros and cons but overall Deep Learning Technology has a very bright future and anyone thinking of getting into this has a very bright future too ,let me know your views on deep learning in the comment section. Thank you 

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