A quick guide to deep learning with Python
Deep learning is part of the broader application of machine learning, which, in turn, is a subset of artificial intelligence (AI). Generally, deep learning deals with algorithms that seek to mimic the structure and functioning of the human brain.
To do this, deep learning uses complex programming algorithms called artificial neural networks. You can read more about this in The Definitive Guide to CNVRG. These algorithms are used to build intelligent computer models that can analyze random data and solve complex problems on their own. Additionally, deep learning is often used 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 is essentially an attempt to mimic and simulate the functions of the human brain, hence the term “neural”. A neural network consists of several structural and functional layers, each layer consisting of several nodes.
These nodes are also sometimes called neurons because they are believed to perform the role that neurons have in the human brain. The flow and processing of information from one layer to another is determined by the connections between the multiple nodes of the neural network.
To simulate the role and functions of the nodes, the neurons are assigned weights. Weight here refers to the weight that nodes give to the input data fed into them. 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 them. When processing is complete, the neural network produces an output.
A neural network is generally composed of three main layers:
- Input layer – The role of this layer is to receive sets of input data.
- Hidden layer – The role of this layer is to extract important features, correlations or patterns in a given data set. It does this by detecting data characteristics, aspects, and mathematical or logical patterns if there are any. It is the brain’s central processor of the neural network. It is 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 uses several different types of mathematical functions. These functions are used to process and make sense of 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 dataset which is fed into the neural network after the input data has been passed through the weights and biases assigned to the nodes. The weighted product is calculated by multiplying the input value and the weight assigned to the neuron traversed by the input data.
The neural network then sums 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 by a particular function. Here are some examples of the different types 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 occur. Its value is between 0 (will not happen) and 1 (will definitely happen).
- Threshold function – This function assigns a threshold value to activate the function. If the value of input x is greater than the threshold value, the function is activated.
- Rectifier function – This is one of the most used functions. If the value of x is positive, it gives x as 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 interval (-1.1).
- Cost function – The cost function gives the difference between the actual output taken from the training dataset and the predicted output from the neural network. Cost here refers to the loss of precision between the predicted output and the actual output. Programmers can adjust the weights and biases of neural networks to arrive at the least expensive value. They can make iterative adjustments whenever the need arises throughout the training process.
Python as a 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 needed to develop a network model using deep learning with Python:
- Import the required libraries. Python has an extensive built-in database of libraries consisting of functions used to process input data.
- Load the dataset. The next step is to load the dataset into your model.
- Check the total number of training and test samples.
- Visualize the data. Python has applications and tools that will allow you to create data visualizations.
- Build the model. The next step is to create the mode by running your dataset through the existing neural network framework.
- Implement loss and optimization.
- Test the model and improve accuracy. The final step is to test the model and determine its accuracy. You can always improve the model by making further adjustments.
Deep learning with python is relatively simple and straightforward. You just need to understand what it’s supposed to do, what it does to data, and how it handles data in general. The rest can be done by Python once you learn how to use libraries and algorithms to process data through deep learning.