A challenging aspect of deep learning solutions is understanding the knowledge and decisions made by deep neural networks. At the same time, the interpretation of decisions made by neural networks has always been difficult for humans. With the rise of deep learning and the proliferation of big data, the problems have become more complex. Start to understand neural networks because this is the future of computer science.
Understanding Neural Networks
Imagine a neural network composed of hundreds of millions of neurons performing a deep learning task, like recognizing images. Usually, you are interested in understanding how the network makes specific decisions. Most of the current research has focused on detecting what active neurons are in the network.
Understanding decisions in neural networks has focused on three areas: feature visualization, attribution, and dimensionality reduction. In particular, Google has published some remarkable tools and research in the feature visualization space. This article is an overview of the basics information of neutral networks.
What are Neural Networks?
An artificial neural network learning algorithm, neural network, or just neural net is a computational learning system. It uses a network of functions to understand and translate a data input of one form into the desired output. The Artificial Neural Network concept was inspired by human biology and how neurons in the human brain work together.
Overall, machine learning algorithms use a variety of tools and approaches, including neural networks. Many machine learning algorithms employ neural networks for processing data inputs into a space that computers can understand.
Artificial Neural Network Models
There are many types of neural networks available, or that might be in the development stage. In a classification neural network, factors to consider: Structure, Data flow, neurons density, layers, and depth activation filters. Here are some modeling of neural network
- Feed Forward Neural Networks
- Multilayer Perceptron
- Convolutional Neural Network
- Recurrent Neural Networks
- Sequence to sequence models
- Modular Neural Network
Structure of Artificial Neural Network (ANN)
Human brains work and make good decisions, and this is the basic premise behind ANNs. Neurons are part of the human brain, which is composed of 86 billion nerve cells. It also connects thousands of cells through axons, despite receiving input from various sensory organs. Furthermore, it creates electric impulses and which is travel through the Artificial neural network. Communication between neurons is necessary for handling different issues.
As a result, we can say that a distributed network consists of several nodes. These neurons mimic the biological neurons of the human brain despite being associated with links. They interact with each other, although nodes take input data. Further, performing simple operations on the data results, these operations pass to other neurons. Also, the output at each node is called its activation or node value. They are capable of learning that takes place by altering weight values. Hence, the following illustration shows a simple Artificial Neural Network (ANN)
Starting to Build Your First Neural Network
The section is about some tips about how you can create your first neural network. It is the first step in creating a neural network to create output based on input data. Using weighted sums, you will come up with the answers to those questions. First, you’ll need to represent the inputs using Python and NumPy.
Here are the steps you should follow to build your neural network.
Wrap the Inputs of Neural Network
NumPy is a Python library that supports large, multi-dimensional arrays and matrices. You will use it to represent the inputs of the network as arrays. Nevertheless, you might want to consider playing with the vectors in pure Python before you use NumPy.
As this is your first neural network, you’ll keep things simple and build a network with just two layers. You can see that the neural network has only two operations inside it: the dot product and sum. You can add more layers but try to keep using only linear operations. Layers are consistently correlated with the input layer, so adding more output layers will have no effect. Therefore, a network with more layers will always predict the same results as one with fewer layers.
Train Your Neural Network
When you train a neural network, you first assess the error and then adjust the weights accordingly. To do that, you’ll need to use gradient descent and backpropagation algorithms. Apply gradient descent is to find the direction and the rate to update the parameters.
Compute The Prediction Errors
To understand the magnitude of the error, you need to choose a way to measure it. And for that purpose, the cost function or loss function will work to measuring the errors.
Reduce The Errors
It is vital to change the weights and bias variables to reduce errors. To fully understand it, you need to modify the weight variable, with the bias remaining constant.
Applying The Chain Rule
In your neural network, you need to update both the weights and the bias vectors. The function you’re using to measure the error depends on two independent variables, the weights and the bias. Since the weights and the bias are independent variables, you can change and adjust them.
Adjust The Parameters With Backpropagation
In this section, start with updating the bios for walking through the backpropagation process step by step.
Create The Neural Network Class
So now you know how to update the weights and biases by writing expressions. It’s the right time for you to create a class for the neural network.
Train Your Network With More Data
In the last step, you need to adjust the weights and bias for one data instance. Nonetheless, your final goal is to generalize the network to a broader dataset.
Adding More Layers to the Neural Network
Deep learning models usually require a large amount of data since the datasets are complex and have many nuances.
This is the way how you can design your artificial neural network after understanding it.
Why Use Neural Networks?
Neural networks are also suitable to help people solving complex problems in real-life situations. These algorithms allow them to learn and model nonlinear and complex relationships between inputs and outputs. As a result, the neural network is capable of improving decision processes. Here are some examples of the use of neural networks:
- Fraud detection for credit cards and Medicare.
- Logistics optimization in transportation networks.
- A targeted marketing strategy.
- Predictions of the financial market
- Natural language processing, also known as character and voice recognition.
- Analyzing ecosystems.
- The use of computer vision to interpret photos and videos.
- Diagnoses and treatment of diseases.
- Control systems for robots.
- Forecasting electrical load and energy demand.
- Quality control of any processing.
- Identifying chemical compounds.
How do you explain a neural network?
A neural network is a series of algorithms that try to mimic the way the human brain operates. By definition, neural networks are systems of neurons, either organic or artificial. Neural networks are a set of input, output, and hidden layers of highly interconnected processing elements, which can transform the data to generate its understanding.
When Should We Use Neural Networks?
ANN (Artificial Neural Network) is used for forecasting, time series analysis, sentiment analysis, and other text processing applications. Each layer of a feedforward neural network is connected to every layer above it. And a layer of information always passes only in the forward direction.