Neurons in Deep Learning—The Building Blocks of Neural Networks

Artificial neural networks (ANNs) mimic human brain networks and process data through neurons. Neurons—called nodes—are fundamental components of deep learning models because they can transfer data throughout various models. Expand your knowledge of how neurons work within deep learning models, learn more about best practices for working with neurons, and discover the different uses of neurons within various fields. 

What are neurons in deep learning?

Deep learning refers to machine learning that utilizes neural networks to replicate human decision-making. Neurons are nodes within the neural network that serve as connection points. A combination of data sets, weights, and biases works to identify, classify, and describe objects within the data. 

How neurons work in deep learning

Neurons operate in various ways, including forward propagation, backpropagation, and activation functions. Forward propagation occurs when each layer of the deep learning network builds on the previous layer to streamline the process of predictions or categorizations, moving forward through each layer. Backpropagation utilizes algorithms to identify prediction inaccuracies and adjusts weights and biases by moving backward through the layers of the deep learning network to train the model. Activation functions influence the network’s decision-making by facilitating data training and improving the neural network’s convergence. 

Structure of a neuron

The components of a neuron consist of deep learning layers, including: 

• Input: This layer of deep learning models receives data for processing. 

• Output: This layer is the final prediction based on the data or classification of the data. 

• Synaptic weight: Weights refer to the strength of the connection between neurons. 

• Activation function: Establishes the output of a neuron based on its input. 

Types of neurons

Various types of neurons commonly used in deep learning include:

• Feedforward neurons: Neurons within feedforward neural networks (FFNNs) connect so that the information flows forward, starting from the input layer and ending at the output layer. 

• Convolutional neurons: Neurons within convolutional neural networks (CNNs) gradually improve and learn from their data and training. CNNs can detect spatial features, so they often process data such as images and sound.

• Recurrent neurons: Neurons within recurrent neural networks (RNNs) take information from prior inputs (the neuron’s “memory”) to determine current inputs and outputs.

Who uses neurons in deep learning?

Various professionals, including physicists, financial advisors, and medical researchers, rely on neurons in deep learning. Physicists utilize neural networks to detect events within particle colliders, providing them with cost-effective and efficient research. Banks and financial advisors utilize neural networks for predictive analytics and financial forecasting. Medical researchers utilize neural networks to help with cancer screening and other medical applications for patients. 

Best practices for working with neurons

You can optimize the use of neurons by choosing the correct activation function and implementing regularization techniques. Explore these best practices in more detail below.

Choosing the right activation functions

Activation functions are mathematical functions that determine the output of a neuron based on its input and find patterns in the input data within neurons. These functions essentially “activate” the neuron. The activation function is the last step in a neural network before producing an output. 

Activation functions are either linear or nonlinear. Linear activation functions are linearly dependent on each other and learn with less complexity than nonlinear functions. A nonlinear activation function introduces additional complexity to a neural network and helps it learn how to approximate a larger group of functions.

The most popular activation functions include sigmoid, ReLU, and tanh activation. 

• Sigmoid: Sigmoid functions are nonlinear activation functions that map out any given input to a value between zero and one. This assists in stabilizing the training of neural networks and mapping values to produce a correct output. 

• Rectified linear unit (ReLU): ReLU activation functions involve simpler mathematical operations, which work much faster than sigmoid and tanh activation functions. These mathematical functions replace negative values with zero and maintain positive values. 

• Tangent hyperbolic (tanh): Tanh activation functions work similarly to sigmoid functions, but their input-to-output values range from negative one to one and are centered at zero. Tanh functions use hidden layers within a neural network to pass better input values to the next hidden layer. 

Your choice of function depends on various aspects, including the type of neural network you’re working with, which machine learning tasks you must take into account, and the neural network's architecture. If you are a beginner working with convolutional neural networks, the ReLU activation function is a great place to start. This is due to its simplicity, effectiveness, and speed. If you are working with a recurrent neural network, use the sigmoid or tanh function because the ReLU function wouldn’t work with a recurrent network’s architecture. 

Regularization techniques

Regularization techniques help machine learning models prevent overfitting within neural networks. Regularization techniques prevent overfitting by adding bias and adjusting weight values in machine learning models. After implementing regularization, the models perform better on their training data. The two regularization techniques to choose from include: 

• L1 regularization: This form of regularization forces weights to zero, which helps to reduce overfitting. L1 regularization also takes the absolute value of weights within a neural network, increasing the cost linearly. 

• L2 regularization: This form of regularization takes the square of the weights within a neural network, exponentially increasing the cost of the outliers. 

L1 regularization is beneficial for minimizing unnecessary features within a machine learning model, but L1 doesn’t always work well for training every machine learning algorithm. L2 regularization doesn’t give you explicit feature selections like L1, but it's usable for training types of machine learning models when all features need to remain present. You should test each form of regularization yourself to see which one best fits your needs. You can learn how to utilize regularizers for L1 and L2 regularization with TensorFlow’s tutorials on their website. 

Discover more about neurons in deep learning with Coursera

Neurons are the essential building blocks of neural networks and deep learning models. Expand your knowledge of neural network architecture on Coursera with DeepLearning.AI’s Neural Networks and Deep Learning, or you can learn more about implementing deep learning processes with Keras through IBM’s Introduction to Deep Learning and Neural Networks with Keras.