How Neural Networks work in Machine Learning ? Understanding what is Neural Networks

Summary

In this video, Jay Patel explains the concept of neural networks by drawing a parallel to the human brain's neuron network. He discusses how neural networks recognize patterns in input data to make predictions. The video covers the structure of a neuron, the role of activation functions, and the organization of layers in a neural network. It also touches on the importance of weights and biases in determining the network's output. The explanation is aimed at helping viewers understand the basics of neural networks before diving into more complex topics in subsequent videos.

Takeaways

• 🧠 **Understanding Neural Networks**: Neural networks mimic the human brain's ability to recognize patterns through a network of neurons.
• 👶 **Learning by Example**: Just like a baby learns to recognize fruits by seeing them repeatedly, neural networks learn patterns from input data.
• 🍎 **Pattern Recognition**: Neural networks can be trained to recognize patterns in images, such as distinguishing between apples and oranges.
• 💡 **Neuron Function**: A single neuron in a network functions as a simple function that outputs a value between 0 and 1, representing pattern recognition.
• 🌐 **Activation Functions**: Neurons are also known as activation functions that determine the output value based on the weighted sum of inputs.
• 📊 **Layered Structure**: Neural networks are composed of input, hidden, and output layers, each responsible for different aspects of pattern recognition.
• 🔢 **Input Layer**: The input layer's neurons correspond to the features of the input data, such as pixel values in an image.
• 🍏 **Output Layer**: The output layer provides a prediction, with the value indicating the probability of the input belonging to a certain class.
• 🤔 **Hidden Layers**: Hidden layers are crucial for capturing complex patterns and can vary in number and complexity based on the task.
• 🔗 **Connections and Weights**: Each neuron is connected to others with weights that are adjusted during training to improve pattern recognition.
• 🔄 **Training Process**: Neural networks start with random weights and are trained to adjust these weights to accurately predict outcomes.

Q & A

What is the primary function of a neural network?

The primary function of a neural network is to recognize patterns in input data and make predictions based on those patterns.

How does the human brain's neural network relate to artificial neural networks?

The human brain's neural network, with billions of neurons and trillions of connections, serves as an inspiration for artificial neural networks, which also use a network of neurons to recognize patterns.

What is the role of a single neuron in a neural network?

A single neuron in a neural network processes input data and produces an output value, typically between 0 and 1, which contributes to recognizing patterns in the data.

How do neurons recognize different patterns?

Different neurons store different values, which are responsible for recognizing various patterns. For instance, some neurons might activate for recognizing red color, while others might activate for orange.

What are the three types of layers in a neural network?

The three types of layers in a neural network are the input layer, the hidden layer, and the output layer.

How is the number of neurons in the input layer determined?

The number of neurons in the input layer is determined by the number of features in the input data. For example, if an image is 28x28 pixels with an RGB color schema, the input layer will have 2352 neurons.

What is the significance of the output layer in a neural network?

The output layer provides the final prediction, with each neuron holding a value between 0 and 1, indicating the probability of the input data belonging to a certain class.

What are hidden layers and what is their purpose?

Hidden layers are the layers between the input and output layers that are responsible for recognizing and holding patterns from the input data.

How are the number of neurons and layers in a neural network determined?

The number of neurons and layers in a neural network depends on the complexity of the task and the amount of information that needs to be recognized. More complex tasks may require more neurons and layers.

What is the role of weights in a neural network?

Weights are the parameter values assigned to the connections between neurons. They determine the emphasis given to certain patterns or regions in the input data.

How are the weights in a neural network initially set and how are they adjusted?

Weights are initially set to random values. They are then adjusted through the training process, where the model learns to change the weight values to improve prediction accuracy.

Outlines

00:00

🧠 Understanding Neural Networks

Jay Patel introduces the concept of neural networks by comparing them to the human brain's neuron network, which recognizes patterns in experiences. He explains how neural networks process input data to identify patterns and make predictions on new data. The video uses the example of classifying images of apples and oranges. Patel also discusses the structure of a neuron, its role in pattern recognition, and how different neurons activate for different features, such as color. He further explains the layers of a neural network: the input layer, which holds data values; the hidden layer, which processes patterns; and the output layer, which provides the final prediction. The number of neurons and layers can vary depending on the complexity of the task.

05:01

🔗 Connections and Weights in Neural Networks

This paragraph delves into the connections between neurons and the concept of weights, which are parameters that the model learns during training. Weights influence the importance of certain patterns in the data. The process of calculating a weighted sum is described, along with the role of bias, which adjusts the weighted sum. Activation functions are revisited as they transform the weighted sum into an output value. The video explains how weights are initialized randomly and then adjusted through training to improve the model's predictions. The script ends with a teaser for the next video, which will cover the training process in more detail.

Keywords

💡Neural Network

A neural network is a computational model inspired by the way the human brain processes information. It is composed of interconnected layers of nodes, or 'neurons,' which transmit signals between each other. In the video, the neural network is used to explain how a machine learning model can learn to recognize patterns, such as distinguishing between images of apples and oranges, much like a baby learns to recognize fruits.

💡Human Brain

The human brain is the central organ responsible for cognition, learning, and memory. It contains billions of neurons and trillions of connections. The video uses the human brain as an analogy to introduce the concept of a neural network, highlighting how both the brain and artificial neural networks recognize patterns in data.

💡Neuron

A neuron is the fundamental unit of a neural network, analogous to a nerve cell in the brain. It receives input, processes it, and produces an output. The video simplifies a neuron as a function that stores a value between 0 and 1, which contributes to pattern recognition within the network.

💡Activation Function

An activation function in a neural network determines the output of a neuron given an input or a set of inputs. It introduces non-linear properties to the network, allowing it to learn and model complex patterns. The video mentions that neurons, when activated, can be thought of as activation functions.

💡Input Layer

The input layer of a neural network is the first layer that receives the raw data. It consists of neurons equal to the number of features in the input data. In the context of the video, if the input is an image, the input layer would consist of neurons representing the pixel values of that image.

💡Hidden Layer

Hidden layers are the layers between the input and output layers in a neural network. They are not directly visible to the outside and are where the network processes and transforms the input data. The video explains that hidden layers can recognize patterns such as shapes and colors in images.

💡Output Layer

The output layer is the final layer in a neural network that produces the result of the network's computations. It contains neurons that correspond to the possible outcomes. In the video, the output layer has a single neuron to determine whether an image is an apple or an orange.

💡Weights

Weights in a neural network are the numeric values that represent the strength of the connections between neurons. They are adjusted during the training process to improve the network's performance. The video uses the analogy of emphasizing certain patterns to explain how weights influence the network's predictions.

💡Bias

Bias in a neural network is a parameter that is added to the weighted sum of inputs before passing it to the activation function. It helps to adjust the activation threshold of a neuron. The video explains that bias can affect the weighted sum, influencing the confidence level of the network's predictions.

💡Training

Training in the context of neural networks refers to the process of adjusting the weights and biases through exposure to data to minimize the difference between the predicted output and the actual output. The video mentions that weights are initially set to random values and are adjusted through training.

💡Pattern Recognition

Pattern recognition is the ability of a neural network to identify similarities or regularities in data. It is central to machine learning tasks such as image classification. The video uses the example of recognizing fruits to illustrate how neural networks learn to recognize patterns.

Highlights

Neural networks mimic the human brain's pattern recognition abilities.

Human brain's billions of neurons and trillions of connections are key to pattern recognition.

Artificial neural networks learn to recognize patterns from input data.

Neural networks make predictions on new data based on learned patterns.

A single neuron in a neural network outputs a value usually between 0 to 1.

Different neurons store different values to recognize various patterns.

Neurons can be thought of as activation functions that respond to stimuli.

A neural network is composed of an input layer, hidden layers, and an output layer.

The number of neurons in the input layer corresponds to the features in the input data.

The output layer's neurons represent the probabilities of the output classes.

Hidden layers store patterns and can vary in number based on the complexity of the task.

The number of neurons and layers affects the model's complexity and training time.

Connections between neurons have weight values that are trained during the model's learning process.

Weighted sum is calculated by multiplying neuron values with their corresponding weights.

Bias is a parameter that adjusts the weighted sum's threshold.

Activation functions process the weighted sum to produce output values.

Neurons' output values are used to calculate the next layer's weighted sums.

Weights are initialized randomly and adjusted through training to improve prediction accuracy.

Upcoming videos will cover the training process of neural networks.

Transcripts

what's going on everyone this is jay

patel and in this video we will

understand what is

the neural network to understand the

working of a neural network

let us understand the working of the

human brain first

now the human brain has billions of

neurons and trillions of connections

between these neurons

with the help of this network of neurons

it

always tries to recognize patterns in

anything we see

or experience let's say a baby is

learning about the fruits

at first it does not know about any kind

of fruit but

if he sees an image of a fruit let's say

apple for a certain number of times

its brain starts forming patterns inside

which helps to recognize the apple next

time he sees it similarly the artificial

neural network works

we feed a set of input data and based on

this input data

the network tries to recognize patterns

in it and makes

output prediction for the new data for

example if we pass a bunch of

images of apple and oranges this network

will try to recognize patterns in these

images

and based on this pattern stored it will

make the new predictions

with the image it has never seen before

now let us understand what is a single

neuron

a neurons are the function that gives

some output value

this output value can be anything but

it's usually small and between 0 to 1.

you can think of the neuron as something

that stores a value between zero to one

it can be the simplest explanation of a

neuron now different neurons stores

different values in them

and these different values are

responsible for recognizing different

patterns at the different region

for example there may be some neurons

which hold some numbers

that are responsible for recognizing the

red color in an image of an apple

and there may be some other neurons for

recognizing the orange color in the

image of an apple

so you can say that when we feed an

image of an apple

some neurons get activated and when we

feed an image of an orange some other

neurons will get activated

it's similar to what happens inside a

human brain when we see something that

we like

some neurons get activated and when we

see something that we don't like some

other neurons get activated now due to

these

activations of neurons we can also call

these neurons

as activations and as these are the

functions

we can call them activation functions

now the collection of these neurons

forms

layers a neural network is divided into

three types of layers

the input layer the hidden layer and the

output layer

the input layer has the neurons which

holds the value from the data set

so the number of neurons in the input

layer will be equal to the number of

peters we have in our input data

let's say our image of an apple is of

the size 28 pixels

into 28 pixels and the color schema that

we are using is rgb

thus each pixel will store three values

of red blue and green

each thus the total number of features

in this will be

28 into 28 into 3 which will be

2 3 5 2 and thus the total number of

neurons in the input layer

will be 2 3 5 2 each whole each neuron

holding the color value of every pixel

now as our final

output can only be an apple or an orange

thus the

output layer will have only one neuron

which holds a value between 0 to 1

showing the probability of an image

being an apple or an orange

if the value comes out to be greater

than 0.5 then we will classify it as an

apple if the value comes out to be less

than 0.5 then we will classify it as an

orange

now it brings us to the hidden layers

the hidden layers are responsible for

holding the patterns in them it is

possible here that our first

layer is responsible for finding the

shape of the content of the image

and as you can see the shape of both

apple and orange are slightly different

from each other

and the second layer might be

recognizing the colors in the central

region

and some neurons will be activated for

red color while the other

with the orange now the question is how

many neurons should a layer be having

and how many layers should we be using

can we have three or let's say

five layers as our hidden layers the

answer is it depends on our choice

we can keep as much as we want and as

much as it requires for our application

with the data set of an apple and the

oranges we don't have a lot of

information to recognize

because we can keep only one hidden

layer with only few neurons or we can

also keep

two hidden layers but if our job is to

classify whether the image is in cat or

a dog

then there may be many features involved

here and thus we can keep more number of

neurons in each hidden layer

and more number of hidden layers as well

also one more thing to note here is that

the more the number of neurons and more

the number of layers

larger will be our calculation thus it

will take more time to

train our model so if we can make our

model with few neurons and few layers

then we need not use

more number of layers now you understood

about neurons and the layers

now let us talk about the connections

between every pair of neurons

there is one connection between every

two pair of the neurons and we assign a

weight

value to every pair of the two neutrons

these weight value is nothing but the

parameter that we

train and we call them weights because

they determine how much weight should we

be putting

or how much emphasis should be given to

a certain region

or certain patterns that we are

recognizing

this can be done by taking a weighted

sum now a weighted sum is when we

multiply

every weight within every value of the

neuron and take itself

let's say this neuron is responsible for

recognizing the color in the central

region of the image

now our weighted sum will be high if the

color in the central region of the image

is red thus our network will be

confident about having the presence of

an apple in the image

instead of an orange now we also need to

add

another parameter called bias this bias

value determines

how high a weighted sum should be if the

b value the bias value is negative then

our weighted sum will be less

but if this value is high positive

number then our

weighted sum will be high now do you

remember i talked about the activation

functions

now this weighted sum is then passed to

the actuation function which gives a

proper

output value a single small output value

and this output value only gives the

existence of a neuron

so a21 will be the output value

after passing the weighted sum to the

activation function

it is also good to notice here that

these neurons will again be multiplied

with the weights

to calculate the weighted sum for the

next layer and the process will be

repeated

until and eventually we generate the

final output prediction

which will be a3 in our case now we can

ask a question here and that is we know

that we get the value of the

neurons by taking the weighted sum and

passing it

in an activation function but then how

do we get the values of these weights

the worst thing to do is to set all

these weights

manually and that would be a really

hectic job

and that's it's not a correct approach

so

what we do is that we first initialize

random values to these weights

and then we train our model and after

the model

is trained it will automatically change

the values of the weight

to give the proper output prediction and

in the next video we will find out the

exact way

how it's done so this was about the

neural network

and before you go if you found this

video helpful

then make sure to hit the red subscribe

button and the bell icon

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upload next machine learning video

and in the upcoming videos in this

series we will completely understand the

neural network and we will implement the

whole neural network

in the python so let's jump into our

next video