How Neural Networks work in Machine Learning ? Understanding what is Neural Networks
Summary
TLDRIn 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
🧠 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.
🔗 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.
Mindmap
Keywords
💡Neural Network
💡Human Brain
💡Neuron
💡Activation Function
💡Input Layer
💡Hidden Layer
💡Output Layer
💡Weights
💡Bias
💡Training
💡Pattern Recognition
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
so that you get notified every time i
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
関連動画をさらに表示
Neural Networks Explained in 5 minutes
Backpropagation Solved Example - 4 | Backpropagation Algorithm in Neural Networks by Mahesh Huddar
Unit 1.4 | The First Machine Learning Classifier | Part 2 | Making Predictions
The Essential Main Ideas of Neural Networks
Neural Networks Pt. 2: Backpropagation Main Ideas
Gradient descent, how neural networks learn | Chapter 2, Deep learning
5.0 / 5 (0 votes)