In terms of biology, a neuron is the basic structural and functional unit of the nervous system responsible for transmitting information throughout the body. In Artificial Intelligence, a neural network in Python is a computational unit that processes and transmits information. Moreover, it receives input and performs some calculations and computations on that input and generates the respective output.
From a biological perspective, neurons comprise an axon that sends messages to other neurons or cells, a cell body, and dendrites that receive signals. Both chemical messages known as neurotransmitters and electrical impulses are used by these cells to communicate. Artificial neurons, which are frequently the building blocks of neural networks, imitate this capacity in artificial intelligence (AI) by processing and propagating information using mathematical functions and weights, allowing computers to learn and make judgments that are similar to those of humans.
What is a Neural Network in Python?
The human brain’s structure has inspired developers to make a neural network. However, a neural network is a computational model. In Python, the neural network can be created using several libraries like TensorFlow, Keras, or PyTorch.
Once you have installed libraries, you can create a simple neural network using different libraries. In this case, we are taking TensorFlow as an example.
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
model.add(Dense(units=64, activation=’relu’, input_shape=(input_size,))) #input_size will be determined by user
model.add(Dense(units=32, activation=’relu’) #Hidden layer
model.add(Dense(units=output_size, activation=’softmax’) #Output layer
model.compile(optimizer=’adam’, loss=’categorical_crossentropy’, metrics=[‘accuracy’]) #Compile the model
model.summary() #Print model summary
This code demonstrates the simple feedforward neural network using Keras Sequential API. Moreover, there are a lot more variations that can be done to improve the code.
What are Neural Network Basics?
The purpose of explaining the example before the basics was to generate your focus and curiosity about how things work or how they perform certain tasks.
As explained earlier, neurons are basic units of a neural network, and they somewhat copy the functionality of biological neurons. Adding to that, the neurons receive inputs, apply weights to those inputs, sum them up, and apply an activation function. After doing all that, they produce an output. Softmax was the activation function that was used in our example.
For organizing the neurons, there are layers within a neural network. There are three types of layers:
It receives input data and passes it to the next layer.
Hidden layers are in the middle of the Input and Output layers where calculations, computations, and learning take place.
Produces the final output of the neural network based on the computations from hidden layers.
Neurons in adjacent layers are connected by synapses, connections, or junctions. Each connection has an associated weight which determines the strength of the connection between two neurons.
The weights are learned and adjusted during the training process to optimize the network’s performance. However, thinking about how learning is performed is to observe errors between predicted and actual results.
To introduce non-linearity into the network, the activation functions are utilized. There are quite a few activation functions including ReLU having full name Rectified Linear Activation, Sigmoid, Tanh, and others. With the help of these functions, we get to know whether a neuron should be activated or not based on the weighted sum of inputs.
Training is a process where neural networks learn from data. Moreover, this involves presenting the neural network with input data along with ground truth (correct outputs) and adjusting the weights iteratively. So that the difference between the actual and predicted outputs should be minimum as much as possible. There are a few techniques that perform the functionality of minimalization like gradient descent and backpropagation.
These functions (‘categorical_crosslossentropy’) used in the example above and many others quantify how well the network is performing by measuring the difference between predicted and actual outputs.
Optimization is done so that the value of the loss function should remain minimal by optimizing network parameters i.e. weights and basis using optimization algorithms like gradient descent, Adam, RMSprop, etc.
What is an Input Layer?
The input layer in a neural network is the starting point of the network that directly receives raw input data. In Addition to that, It is an entry point for information to flow through the whole network.
There are certain Key aspects of the input layer.
The data at the input layer varies with the situation and demand. For instance, in image processing tasks, the input can be the pixel value of images, while in natural language processing, it could be word embedding or a sequence of words.
The shape or dimensions of the input data determines the structure of the input layer. For example, for an image of 26*26 pixels, the input shape might be (26*26*1), indicating width, height, and channel which is optional.
What are Deep Neural Networks?
A deep neural network as seen by its name handles complex patterns and representations from data. It is a type of artificial neural network with the characteristics of multiple layers between input and output layers and also hidden layers which allow the deep neural network to understand or learn complex patterns.
In conclusion, I would like to say that the field of artificial intelligence cannot be explained in the space of one article. As we dive into this field, there are rapid developments and every day brings out a new strategy and new concepts which are revolutionizing this world very fast. However, you can learn more about this by reading official research papers.