Source: Deep Learning on Medium
AI vs Machine Learning vs Deep Learning Machine Learning vs Deep Learning
lets see this image which is coming on your screen it will give you a clear view of artificial intelligence.machine learning and deep learning.
In the image you can see three concentric circle which clearly describe that Deep learning is a subset of machine learning
and this machine learning is again a subset of artificial intelligence
is that interesting ? no right now make it in more simple way and
lets move further in topic
lets talk first about Artificai intelligence
In simple words artificial intelligence is just putting human intelligence to machines
Artificial intelligence is replica of a human brain it is the same in which human brain thinks works and function
AI has two different levels
the first one is called as Narrow AI this is a artificial intelligence when a machine perform a specific task better than a human
the Second one is called as General AI in this artificial intelligence reaches the general state when it can perform any task which has the same accuracy level in which human can do
one of the best example of artificial intelligence is Sophia, this is the most advance AI model present today
now moving towards Machine Learning
as the name suggest we are empowering computer system to learn
Machine learning is the best tool which is used to analyze, understand and identify patterns of the data.
One of the main ideas behind machine learning is that the computer can be trained to automate the tasks that would be impossible for a humans.
Machine learning uses data to feed an algorithm that can understand the relationship between the input and the output.
When the machine completes the learning, it can predict the value for the new data point.
Now lets discuss the machine learning process quickly
imagine you have a task to classify the images based on the objects such as bicycle ship car or a plane
The four objects are the class the classifier has to recognize. To construct a classifier,
you need to have some data as input and assigns a label to it. The algorithm will take these data,
find a pattern and then classify it in the particular class.
This task is called supervised learning. In this learning the training data you feed to the algorithm includes a label.
when you train a algorithm you require this few standard steps
1. Collecting the data
2. Train the classifier
3. Make predictions
The first step is necessary and choosing the right data will make the algorithm success or a failure.
The data you choose to train the model is called a feature. In our object classification example, the features are the pixels of all the images.
If your image is a 28×28 size, the dataset contains 784 columns (28×28).
in the picture on the screen the image containing a object has been transformed into a feature vector. The label tells the computer what object is in the image.
The first step consists of creating the feature columns.
Then, the second step involves choosing an algorithm to train the model.
When the training is done, the model will predict what picture will link to what type object.
After that, it is easy to use the model to predict new images.
for each new image that we will feed into the machine it will be now able to predict to which object does this image belongs to
some of the example of machine learning in real life is
Amazon using machine learning to give better product choice recommendations to there costumers based on their choice.
Netflix uses machine learning to give better suggestions to their users of the Tv series movie or shows that they would like to watch.
Now moving towards the last topic which is deep learning.This deep lerning is just the evolution of machine learning.
Just like we use our brain to identify patterns and classify various types of information, deep learning algorithms can be taught to complete the same tasks for machines.
Whenever we receive a new information, the brain tries to compare it with a known item before making sense of it
the same concept is used by deep learning for the machines
The deep learning helps machine to use different layers to learn from the data.
The depth of the model is represented by the number of layers in the model.
Deep learning is the new state of the art in term of AI.
In deep learning, the learning phase is done through a neural network.
A neural network is an architecture where the layers are kept on top of each other
Consider the same image example above. The training set would be fed to a neural network
Each input goes into a neuron and is multiplied by a weight. The result of the multiplication flows to the next layer and become the input.
This process is repeated for each layer of the network. The final layer is named the output layer
it provides an actual value for the regression task and a probability of each class for the classification task.
The neural network uses a mathematical algorithm to update the weights of all the neurons.
The neural network is fully trained when the value of the weights gives an output close to the reality.
Automatic car driving system is a good example of deep learning.
Lets understand difference between machine learning and deep learning in simple example
Suppose we have a flashlight and we teach a machine learning model that whenever someone says dark the flashlight should be on
now the machine learning model will analyse different sentences said by people and it will search for the word dark and as the word comes the flashlight will be on
but what if someone says this sentence I am not able to see anything the light is very dim here the person wants the flashlight to be on
but the sentence does not the consist the word dark so the flashlight will not be on.
Thats where deep learning is different from machine learning. If we have trained our model in deep learning model it would on the flashlight
a deep learning model is able to learn from its own method of computing.
hope you guys are now clear with the diffrence between artificial intelligence
machine learning and deep learning.
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