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Understanding the Fully Connected Feedforward Neural Network

In this article, we will dive into understanding the fully connected feedforward neural network, commonly known as Artificial Neural Network. We will start by discussing the building blocks of the neural network: the Perceptron. The perceptron is an abstraction of the biological neurons present in the living organisms.

The Perceptron is composed of neurons, which we can represent through circles in our diagrams. Each neuron in the neural network is connected to other neurons with specific weights to represent the neural network's connections. The fully connected feedforward neural network has an architecture where each neuron in the current layer is connected to each neuron in the next layer. In this architecture, there are no backward connections between neurons, and hence it is known as the feedforward neural network.

The Terminologies in the Neural Network Architecture

The neural network architecture contains the following terms:

Input Layer

The first layer in the neural network architecture is called the Input layer. It takes the input signal(s) and passes it to the next layer for processing. The input layer is an essential part of the neural network as it defines how the inputs are connected to the neurons in the next layer.

Hidden Layers

If there are additional layers between the input and output layer in the neural network, they are known as hidden layers. The hidden layers help extract features from the input, which ultimately affects the output.

Output Layer

The output layer is the final layer of the neural network. It produces the output of the neural network after the input signal has passed through the hidden layers.


The connections between the neurons in the neural network contain weights that affect the output of the neural network. The weights determine how much each input contributes to the next layer's neuron output and are adjusted during training of the neural network.


Omega represents the weights in the neural network architecture. Each connection between two neurons in the neural network is represented by this weight. During signal propagation, the weight's value is multiplied by the signal value to get the value of the neuron in the next layer.

Understanding Neural Networks: Neurons and Activation Functions

Neural networks have become an increasingly popular tool for solving complex problems in various domains such as computer vision, natural language processing, and pattern recognition. To understand how neural networks work, it is essential to learn about neurons and activation functions.

What is a Neuron?

A neuron is a fundamental component of a neural network used for processing information. It takes multiple inputs, multiplies them by different weights, and returns a single output. The figure below demonstrates a graphical representation of a neuron:


  • A neuron takes multiple inputs, each of which is connected to the neuron through a synapse.
  • Every synapse has a weight assigned to it, which helps to filter out input values.
  • The sum of the weighted inputs forms the input signal, which is processed by the activation function.
  • The output of the activation function is passed to the next layer of neurons in the neural network.

What is an Activation Function?

Activation functions are a critical component of neural networks as they determine what output a neuron should produce based on its input. Different types of activation functions can be used, depending on the problem that the neural network is trying to solve.

  • The function f(x) calculates the output of the neuron, given the input signal.
  • The input signal is the weighted sum of the inputs, which is represented as x.
  • The values of the function f(x) can vary depending on the chosen activation function.

Types of Activation Functions

There are various types of activation functions that can be used in neural networks. Some popular ones include:

  1. Step Function: This function produces a binary output, depending on whether the input value is above or below a particular threshold.
  • Formula: f(x) = 1 (if x > 0), f(x) = 0 (otherwise)
  1. Sigmoid Function: This function produces a smooth sigmoid curve that ranges from 0 to 1.
  • Formula: f(x) = 1 / (1 + e^(-x))
  1. ReLU Function: This function is widely used in convolutional neural networks and rectifies negative input values to 0.
  • Formula: f(x) = max(0, x)

A Simple Example

To illustrate how neurons and activation functions work in practice, consider the following simple example:

Suppose a girl wants to choose a boyfriend based on three criteria - whether he has a car, his taste in music, and his looks. Each of these criteria can be represented as input variables, with values of 1 or 0 depending on whether they are present or not.

A neural network can be trained to help her with the decision-making process. Specifically, we could define a neural network with three input neurons, one hidden layer with two neurons, and one output neuron as follows:

  • The input layer receives the input signals representing the three criteria.
  • The hidden layer processes the input signals using a chosen activation function, such as the ReLU function.
  • The output layer generates an output signal, determining whether the boy meets her criteria or not.

This simple example demonstrates how neural networks can be used to solve real-world problems. However, more complex neural networks can have hundreds or thousands of neurons and multiple hidden layers, making them capable of solving even more complex problems.

Understanding Neural Networks through the Eyes of a Girl

Neural networks have become increasingly popular in recent times due to their ability to mimic the learning process of humans. In this article, we aim to explain neural networks in simple terms using the example of a girl trying to determine whether she likes a guy or not.

Encoding Inputs

In our scenario, the girl has three criteria for liking a guy - if he has a good apartment, if he's good-looking, or if he loves heavy metal music. We encode this criteria as follows:

  • x1: Has a good apartment
  • x2: Is good-looking
  • x3: Loves heavy metal music

If the criteria is met, we assign a value of 1, else 0. For better visualization, we can represent 1 as a tick mark and 0 as a red cross.

The girl has a positive mindset towards the presence of a good apartment and good looks, while she has a negative stance towards heavy metal music. Therefore, we assign a weight of 0.5 for apartment and looks, and -0.5 for heavy metal music.

Signaling to the Neural Network

Once we have encoded the input, next we signal this information to the neural network. If the guy meets all three criteria, we give him a signal of +1, and if he doesn't meet any criteria, we give him a signal of 0.

  • signal = +1: If the guy meets all criteria
  • signal = 0: If the guy meets none of the criteria

For example, if the guy doesn't have a good apartment, then x1 will be replaced with 0 in the input.

Summing Inputs

After receiving the signal, we multiply the inputs with their respective weights and sum them up. This gives us the input to the decision-making neuron.

  • input = x1*w1 + x2*w2 + x3*w3

In our example, the weights for apartment and good looks are 0.5, while for heavy metal music it is -0.5. So, for a guy who has a good apartment, is good-looking, and loves heavy metal music, the input will be:

  • input = 1*0.5 + 1*0.5 + 1*(-0.5) = 0.5

Activation Function

The decision-making neuron uses an activation function to determine whether the girl likes the guy or not. In our example, we use the step function which gives an output of 1 if the input is greater than or equal to 0.5, and an output of 0 if it is less than 0.5.

  • output = 1 if input >= 0.5
  • output = 0 if input < 0.5

So, if the input value is 0.5, then the output will be 1, indicating that the girl likes the guy.

Neural Networks and Decision-Making

Neural networks have been used in a variety of fields ranging from finance to medicine. Their ability to effectively process large amounts of data and provide accurate predictions have made them a useful tool in several industries. One area where neural networks have been particularly effective is in decision-making. In this article, we will explore the basics of how neural networks aid decision-making with some examples.

The Basic Structure of a Neural Network

A neural network consists of multiple interconnected neurons that process information. Each neuron in a neural network has a weight, which is basically a numerical value that determines the importance of that neuron in the decision-making process. The neurons are organized in layers, and the connections between neurons allow them to communicate with each other. A neural network typically has three types of layers:

  • Input Layer: This is where the network receives input data.
  • Hidden Layer: This layer processes the input data and applies weights to the neurons. The hidden layer determines the importance of each neuron and its impact on the output.
  • Output Layer: This layer produces the final output of the network.

How Neural Networks Aid Decision Making

Neural networks are particularly useful in decision-making because they can process large amounts of data and provide accurate predictions. The following examples will help explain how they work.

Example 1

Let's assume a young lady is trying to decide if she wants to date a particular guy. She has a neural network in her mind that helps her in this decision-making process.

  1. The first neuron in her neural network is activated when she sees a guy who is handsome and has a good job.
  2. The second neuron is activated when she realizes that the guy also likes the same kind of music as her.
  3. The neurons in her hidden layer apply weights to each input neuron and determine their respective importance.
  4. The output neuron produces an output value of 0.5, indicating that she is on the fence about the guy.

Example 2

Now let's assume another guy comes along who has no job and no apartment, but he loves the same kind of music.

  1. The neurons in her input layer are activated when she sees the guy.
  2. The hidden layer neurons apply weights to the input neurons and determine their importance.
  3. The output neuron produces an output value of 0.5, indicating that she is on the fence about the guy.

Therefore, even though she is on the fence about both guys, she realizes that if they both love the same kind of music, this might be a good reason to choose one over the other.

Example 3

Let's add another neural network layer to our decision-making model and see how it changes the outcome.

  1. The third neuron in her neural network is activated when she meets a guy who has an apartment and loves heavy metal music.
  2. The first neuron in her hidden layer is activated when she meets a guy with an apartment.
  3. The second neuron in her hidden layer is activated when she meets a guy who loves heavy metal music.
  4. The output neuron produces a value of 0, indicating that she would never date a guy who loves heavy metal music and has an apartment.

Adding another layer to the neural network allowed her to evaluate more complex scenarios and make more informed decisions about who she wants to date.

Understanding Neural Networks: A Simple Example in Python

Neural networks, inspired by the structure and function of the human brain, have become a popular tool in machine learning and artificial intelligence. One example of a neural network is the feedforward neural network. In this article, we will explore the basics of how a simple feedforward neural network operates using Python.

The Feedforward Neural Network

The feedforward neural network is a type of artificial neural network where the flow of information moves in only one direction, from input to output. It consists of an input layer, one or more hidden layers, and an output layer. The neurons in each layer are connected to the neurons in the adjacent layers by weighted connections. The feedforward neural network does not have any feedback connections, meaning that the output of one layer only affects the input of the next layer.

Implementing a Simple Neural Network in Python

To implement a simple feedforward neural network, we will be using Python and the NumPy library. NumPy is a powerful library for working with arrays and matrices in Python, making it an ideal choice for building neural networks.

Here is an example program that demonstrates how a feedforward neural network can be implemented using NumPy:

importnumpyasnp# Define the sigmoid activation functiondefsigmoid(x):return1/(1+np.exp(-x))# Define the neural network architectureinput_size=2hidden_size=3output_size=1# Initialize the weights for the input layer and the hidden layerinput_weights=np.random.normal(size=(input_size,hidden_size))hidden_weights=np.random.normal(size=(hidden_size,output_size))# Define the inputinput_data=np.array([[0,1]])# Calculate the weighted sum of the input layerhidden_layer_weighted_sum=np.dot(input_data,input_weights)# Apply the activation function to the weighted sum of the input layerhidden_layer_activation=sigmoid(hidden_layer_weighted_sum)# Calculate the weighted sum of the hidden layeroutput_layer_weighted_sum=np.dot(hidden_layer_activation,hidden_weights)# Apply the activation function to the weighted sum of the hidden layeroutput_layer_activation=sigmoid(output_layer_weighted_sum)# Print the outputprint("Output:",output_layer_activation)

In this program, we first define the sigmoid activation function, which is used to calculate the activation of each neuron in the network. Next, we define the architecture of the neural network. In this example, our network has an input layer with two neurons, one hidden layer with three neurons, and an output layer with one neuron. We then initialize the weights for the input layer and the hidden layer using the NumPy random.normal function.

We define the input data and calculate the weighted sum of the input layer by taking the dot product of the input data and the input weights. We then apply the activation function to the weighted sum of the input layer to calculate the activation of the neurons in the hidden layer. We repeat the process for the output layer and print the output of the neural network.

Implementing Neural Networks with Matrix Multiplication

In order to find information on the internet about neural networks, there is plenty of material available. However, we will focus on using vector and matrix multiplication to implement our neural network.

Firstly, we need to write an auxiliary activation function. This activation function returns 0 if x is less than 0.5 and returns 1 in all other cases.

Here are the steps we take to process an input signal through our neural network:

  1. We form the input signal vector based on three parameters: house, rock, andunter.

  2. These parameters can take on two values, either 1 or 0.

  3. Next, we specify the weights for the first neuron of the hidden layer and the second neuron of the hidden layer.

  4. We then merge these weights into a matrix. This matrix has 2 rows and 3 columns because we have 2 neurons in the hidden layer, and each neuron has 3 input connections.

  5. Next, we construct the connection vector for the output neuron.

  6. We calculate the sum for the hidden neurons by performing matrix multiplication. The input vector x is multiplied by the weight matrix, and we get a vector consisting of sums on each neuron.

  7. Finally, we pass this vector through the activation function and get the output vector that determines whether or not there is a match or affinity.

By following these simple steps, we can successfully implement a neural network using matrix multiplication.

Neural Network Architecture and Functioning

Neural networks are a form of machine learning that enable machines to learn from data and improve their performance over time. A neural network consists of multiple interconnected layers of neurons, each performing a specific function in the data processing pipeline. In this section, we will describe the architecture of a neural network and how it functions.

Neural Network Architecture

A neural network is composed of three main layers: the input layer, hidden layer, and output layer. The input layer receives data from the external environment or other systems and passes it on to the hidden layers. The hidden layer carries out calculations on the input data and processes it in accordance with the activation function. The output layer returns the results of the processing to the external environment or other systems.

Neural Network Functioning

The functioning of a neural network involves forwarding the input data through the network's layers, performing calculations, and passing the results to the next layer until the output is produced. This is referred to as the forward propagation phase. The following steps describe the process in detail:

  1. The input data is passed through the neural network layers.
  2. The hidden layer neurons process the input data using the activation function, and the results are passed on to the output layer.
  3. The output layer neurons compute the final output of the neural network based on the processed input data.

Activation Function in Neural Networks

An activation function is a mathematical function that is applied to the output of each neuron in the hidden layer. It serves to activate the neurons and modulate their output based on the input data. There are several types of activation functions, including Sigmoid, Tanh, ReLU, and Softmax. Each of them has its advantages and disadvantages, depending on the specific application of the neural network.

Understanding Neural Networks: Basic Principles

Neural networks are a type of machine learning algorithm that can process large amounts of complex data. In this article, we will explore the basic principles of neural networks and how they work.

Output of the program

To start, let's take a look at the output of a neural network program:

``` If the output is "I like you", print it to the console. If not, we will call you. We run the program and see what happens. Of course, the output will be "I like you". If you have a flat, you are even more attractive to this person. They are ready to listen to you for hours.

Now let's make the person unattractive, while leaving all other parameters unchanged. We run the program again, and this time we get a call. This illustrates the importance of the attractiveness parameter in determining how our program responds.

Key Takeaways

  • Neural networks are a powerful type of machine learning algorithm.
  • The output of a neural network program depends on its parameters, such as attractiveness.
  • Understanding the importance of these parameters is crucial for effective use of neural networks.

Understanding the fully connected feedforward neural network's architecture and terminologies is vital to building and training neural networks effectively. with this knowledge, one can understand how each neuron in the network plays a crucial part in processing the input signal and producing the final output.
In conclusion, we have seen how neural networks can be used to make decisions based on inputs encoded as binary values. although our example was a bit simplistic, this approach can be used to solve more complex problems that involve decision-making based on multiple criteria.
Neural networks have been shown to be highly effective in aiding decision-making processes. they are particularly useful in situations where large amounts of data need to be processed and analyzed quickly. in addition, by adding additional layers to the neural network, more complex scenarios can be evaluated, allowing for even more informed decisions to be made.
In this article, we explored the basics of how a simple feedforward neural network works using python. we discussed the architecture of the feedforward neural network and how to implement it using the numpy library in python. by understanding how neural networks work, we can apply them to a variety of machine learning and artificial intelligence applications.
The architecture and functioning of a neural network are crucial for its performance and effectiveness. understanding these concepts is essential for designing neural networks that can address real-world problems effectively.

In this article, we've explored the basic principles of neural networks and how they work. Through our example of a simple program, we saw the importance of parameters such as attractiveness in determining the program's output. With this understanding, we can begin to apply neural networks to more complex applications and data.

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