 entire world for machine to recognise, but that the same time, you can’t not give any information about people for a machine to learn from itself, that would take forever. So, what can you do? Well, you can actually combine both types of learning and use something called, “Semi-Supervised Learning.” So, what exactly is semi-supervised learning. This is essentially where you would give data to a machine to learn from with some labels. For example, you can train images of parts of the body, like the eyes or the nose. This way, the machine can develop an understanding of what features a human has. After a ton of training, the machine will finally be able to detect human faces properly.
Let’s dive Deeper into Machine Learning
Now, the real question is, how does the machine actually learn and process this data that we give it? This is where we can go deeper into machine learning and talk about Deep Learning. What exactly is deep learning? You see our brain is made up of several neurons and are all connected to each other. People decided that if they could replicate what is happening in our brain, then they can essentially create a machine that can learn certain things. This thing inside our brain is called, and neural network, which was replicated into deep learning called and artificial neural network(ANN). So, how does this ANN actually work?
In order to properly understand what a Neural network is in deep learning, let’s look back at our example with Timmy the Robot. When we give Timmy an image of a dog, that would be the input. The answer which could either be dog or cat, is going to be the output. So Timmy will have to look at the picture, and analyze every detail of the image. Depending on what a machine analyzes, it will change the values of each hidden layer. The hidden layers are what is in between the input and output layers. These layers can vary depending on what you want the machine to learn/do. Each part of the input layer is connected to each part of the first hidden layer, and each part of the last hidden layer is connected to each part of the output layer, and all the hidden layers between are connected to each other. They are all connected by synapses which change to value of each layer depending on the previous one.
Weights & Biases
There are two key parts in a Neural Network that you must know and those are weight and biases, but what even is that?
As I mentioned before, when we give an input to a machine it changes the value of each layer depending on the previou input. This is basically what a weight is, it decides how much influence the input would have on the output. Now, what are biases? Biases will always add an additional input to the next layer that always have a value of 1. In turn, weights are what decides the changed value, whereas biases add value to any input with the value of one.