Deep learning is a part of the larger application of machine learning, which, in turn, is a subset of artificial intelligence (AI). Generally, deep learning deals with algorithms that seek to imitate how the human brain is structured and how it works.
To be able to do this, deep learning makes use of complex programming algorithms called artificial neural networks. You can read more about this in CNVRG’s definitive guide. These algorithms are used to build intelligent computer models that can analyze random data and solve complex problems on their own. Moreover, deep learning is often employed to analyze and make sense of unstructured data.
What Is A Neural Network?
A neural network is a virtual machine built and developed through programming frameworks such as Python. It’s basically an attempt to imitate and simulate the human brain’s functions, hence the term ‘neural.’ A neural network consists of several structural and functional layers, with each layer made up of multiple nodes.
These nodes are also sometimes called neurons because they’re supposed to play the role that neurons have in the human brain. The flow and processing of information from one layer to another are determined by the connections between the multiple nodes in the neural network.
To simulate the role and functions of nodes, the neurons are assigned weights. Weight here refers to the weight that nodes give to the input data that is fed into it. The neural network feeds the nodes or neurons with input information from the unstructured data set. The neurons or nodes would then process the data fed into it. Once it’s done with processing, the neural network yields an output.
A neural network is typically composed of three main layers:
- Input Layer – The role of this layer is to receive input data sets.
- Hidden Layer – The role of this layer is to extract the important features, correlations, or patterns in a given data set. It does this by detecting data features, aspects, and mathematical or logical patterns if there are any. This is the central processor of the neural network’s brain. It’s responsible for processing the input data by applying the weights, biases, and functions programmed into the neural network.
- Output Layer – This layer will generate the output data.
Functions In A Neural Network
A neural network employs multiple different kinds of mathematical functions. These functions are used to process and make sense of the input data. One of the most important functions used in neural networks is called the activation function.
Activation Function – The activation function is applied to the data set that is fed into the neural network after the input data has been made to pass through the weights and biases assigned to the nodes. The weighted product is computed by multiplying the input value and the weight assigned to the neuron that the input data passed through.
The neural network then adds up all the weighted products to find the weighted sum. A unique bias assigned to the neuron is added to the weighted sum. The final sum is then processed through a particular function. Here are some examples of the different kinds of activation functions that can be used in a neural network:
Types Of Activation Functions
- Sigmoid Function – The sigmoid function is used when the purpose of a model is to predict the level of probability that an event will happen. Its value is between 0 (not going to happen) and 1 (certainly going to happen).
- Threshold Function – This function assigns a threshold value to activate the function. If the value of the input x is greater than the threshold value, the function is activated.
- Rectifier Function – This is one of the most commonly-used functions. If the value of x is positive, it gives x as the output; otherwise, the output is 0.
- Hyperbolic Tangent Function – The hyperbolic tangent function and the sigmoid function are similar. However, the hyperbolic tangent function is bound by the range (-1,1).
- Cost Function – The cost function yields the difference between the actual output taken from the training data set and the predicted output of the neural network. Cost here refers to the loss of accuracy between predicted output and actual output. The programmers can adjust the weights and biases of the neural networks to arrive at the least-cost value. They can make iterative adjustments whenever the need arises during the entire training process.
Python As Development Platform
Python is one of the most popular development platforms used to develop and train artificial neural networks through deep learning. Here are the steps that you need to develop a network model using deep learning with Python:
- Import the required libraries. Python has a vast built-in database of libraries composed of functions used to process input data.
- Load the dataset. The next step is to load the data set to your model.
- Check the total number of training and testing samples.
- Visualize the data. Python has applications and tools that will enable you to come up with visualizations of the data.
- Build the model. The next step is to build the mode by running your data set through the neural network’s existing framework.
- Implement loss and optimization.
- Test the model and improve accuracy. The final step is to test the model and find out its accuracy. You can always improve the model by making further adjustments.
Conclusion
Deep learning with python is relatively simple and straightforward. You just need to understand what it’s supposed to do, what it does to the data, and how it processes the data in a general sense. The rest can be done by Python once you learn how to use libraries and algorithms to process data through deep learning.
