Artificial Neural Networks: Have you noticed how no matter what the written form of a number or letter, or the drawing of a figure, your brain readily recognizes the meaning by the similarity of that representation to your objective concept? It is such a simple and intuitive act, right?
What Is A Neural Network?
An artificial neural network is a programmed organization of algorithms simulating the neurons of a human brain to convert diverse data into readable patterns to recognize attributed meanings.
Machine learning uses neural networks, which perform intelligent behavior from the interactions between its processing steps: data entry, pattern reading, and recognition response.
The most used languages for the assembly of artificial neural networks are Python, Scratch, R, and learn. Still, different from what neural networks think to be a very advanced topic or far from everyday life, there are also simple configurations in Excel capable of generating functional neural networks.
A Learning History
A psychiatrist and a mathematician, in 1943, began studies and experiments on the neural network. Warren McCulloch and Walter Pitts published “Formal Neurons,” which they later modeled with a simple neural network made of electrical circuits. These “neurons” had only one output, a function of the sum of their various inputs.
In second place came the Hebbian Theory, through its book “The organization of behavior,” described synaptic plasticity, that is, the adaptation of brain neurons in the learning process:
“When an axon from the A cell is close enough to excite the B cell and repeatedly or persistently goes on causing the cell to fire, some growth process or metabolic change occurs in one or both cells in such a way that increases the efficiency of the cell. A, as one of the cells capable of causing B to fire.”
Thus, he introduced the concept of reverberation activity that will be explained later on for the functioning of neural networks in Artificial Intelligence.
The Perceptron, an artificial neural network of the feedforward type, appeared in 1958 by Frank Rosenblatt. This linear pattern classifier maps its input through a vector of real value to an output function in the simple binary value.
By the “weight” information transmission efficiency variants, the answers are evaluated and corrected in a retroactive process, thus trained and continuously improved.
The Artificial Synapses
Perceptron networks are the oldest and simplest form of advancing neural networks. The set of weights that connect the inputs and outputs form multiple layers (MLP), and the sum of the multiplication of these weights with the information results in each output node. A binary answer is expected as a yes or no explanation for the analyzed identifications.
For example: When inserting a handwritten number 9, it is expected that, by separating the pixels, the curvatures and pieces of the line drawn will be analyzed. Then, reading patterns such as “circle at the top of the digit” and “straight line to the right” will be formed so that all numbers other than nine will result in 0 in the output, while it results in 1.
Intermediate neurons belong to the hidden layers, which perform data interpretation. It turns out that, instead of resulting in perfectly binary responses, the outputs have many intermediate values at the threshold, and the recognition does not work.
Therefore, the neural network costs are calculated, given by the error difference (expected number – result number). In the form of backpropagation, known as backpropagation in English, they are applied proportionally to the related weights:
- Standard Mode: The weights correction occurs at each entry in the network of an example for training. Each weight correction is based only on the model’s error presented in that iteration. Thus, in each cycle, X corrections occur.
- Batch Mode: One correction is made per cycle. All training examples are computed in the network; their average error is calculated, and based on this error, weight corrections are made.
Such a continuous learning process is Deep Learning or Deep Learning, which, in addition to optimizing a mapping between representative features and a desired output, manages to make the machine automatically learn the usual features from these deep neural networks.
Types Of Neural Networks
Feedforward
In Portuguese: feed forward, advance. The method in which every Perceptron in one layer is connected to every Perceptron in the next layer.
All layers compute the outputs and design each neuron’s firing in the next layer with the activation function in an always linear process.
Convolutional
The feedforward linear architecture does not consider spatial figures and must have their data inferred in training. But the convolutional network uses a starting spatial structure, making its image classification and object detection much more suitable.
It contains five layers: inputs, convolution, clustering, fully connected, and output layers.
Recurrent
They are fostered by a feedback loop connected to their previous decisions, making them capable of continuously making decisions about their inputs. It learns faster by taking its outputs as input and reanalyzing its process autonomously.
They are known as networks with memory, and long-term dependence since all their response is a function of one or more previous responses.
Influence On Mining
In data mining, neural networks automatically extract and organize a large amount of raw data, which takes a lot of effort and time from employees. But also to increase your unstructured database, for example, to start analyzing email texts that are generally unused.
Examples of data mining by neural networks already cover the health field by analyzing 100,000 patient records and learning to recommend the best treatment for a previous diagnosis.
But they have also helped to save lives in the mining industry: to check whether a wall is likely to collapse inside the mines, the worker needs to hit it with an iron bar and analyze its sound.
Now with neural networks embedded in the material and actively learning the state of the walls at every beat, it will be possible to respond with much more precision about their integrity and avoid risks of errors in human analysis.
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