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Machine learning terms you must know about as a beginner.
These terms won't mean anything unless you know what Machine learning is all about.
> Machine learning is the process of making a program which allows a computer to learn from data.
The data could be anything, images, audio or even text.
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> Machine learning is the process of making a program which allows a computer to learn from data.
The data could be anything, images, audio or even text.
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In machine learning we use something called a neural network, this is essentially an imitation of the human brain.
> Neural Networks are a digital imitation of the neurons you see in the human brain.
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> Neural Networks are a digital imitation of the neurons you see in the human brain.
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In these neural networks, data flows through them and each neuron (the circle) has a numerical value which will change.
> The value of a neuron gets changes to something which is close to what we want each time the data passes through the neural network.
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> The value of a neuron gets changes to something which is close to what we want each time the data passes through the neural network.
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Think of the neurons as dials on a lock, you have to tune every dial to open the lock.
It is almost impossible for a human to tune thousands of dials like these, but a computer certainly can.
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It is almost impossible for a human to tune thousands of dials like these, but a computer certainly can.
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Once the dials are well tuned, you have a well trained neural network!
Each dial's numeric value is dependent on a "weight" and a "bias". The weight determines how important the neuron is and the bias make it flexible.
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Each dial's numeric value is dependent on a "weight" and a "bias". The weight determines how important the neuron is and the bias make it flexible.
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So here's a recap of what we've looked at so far:
The neural net is the brain of the machine learning model, the dials you have to adjust to make that neural net work are the neurons.
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The neural net is the brain of the machine learning model, the dials you have to adjust to make that neural net work are the neurons.
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Each time data passes through the neural network, we get to know how wrong it is. The measure of how wrong a neural network is called the "loss". The neural network uses this thing called an "optmizer" to reduce "loss" and tries to get less wrong after each iteration.
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The number of times the data passes through the neural net is called the "epoch".
Let's summarize the entire thing
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Let's summarize the entire thing
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Neural Network: The brain of our machine learning model
Neuron : Each dial in a neural network
Weight : How important the neuron is
Bias : Flexibility of neuron
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Neuron : Each dial in a neural network
Weight : How important the neuron is
Bias : Flexibility of neuron
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Epoch : Number of times the data passes through the neural network
Loss : How wrong the neural net is
Optimizer : Tries to reduce loss and make the neural net less wrong
Congrats! You know a fair bit about commonly used machine learning terms
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Loss : How wrong the neural net is
Optimizer : Tries to reduce loss and make the neural net less wrong
Congrats! You know a fair bit about commonly used machine learning terms
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