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Neural Networks Interview Questions
Neural networks, also known as Artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.
Neural Networks
Neural networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the most well-known neural networks is Google’s search algorithm.
Think of each individual node as its own linear regression model, composed of input data, weights, a bias (or threshold), and an output. The formula would look something like this:

Once an input layer is determined, weights are assigned. These weights help determine the importance of any given variable, with larger ones contributing more significantly to the output compared to other inputs. All inputs are then multiplied by their respective weights and then summed. Afterward, the output is passed through an activation function, which determines the output. If that output exceeds a given threshold, it “fires” (or activates) the node, passing data to the next layer in the network. This results in the output of one node becoming in the input of the next node. This process of passing data from one layer to the next layer defines this neural network as a feedforward network.
Neural networks can be classified into different types, which are used for different purposes. While this isn’t a comprehensive list of types, the below would be representative of the most common types of neural networks that you’ll come across for its common use cases:
The perceptron is the oldest neural network, created by Frank Rosenblatt in 1958. It has a single neuron and is the simplest form of a neural network:
Types of neural networks
Feedforward neural networks, or multi-layer perceptrons (MLPs), are what we’ve primarily been focusing on within this article. They are comprised of an input layer, a hidden layer or layers, and an output layer. While these neural networks are also commonly referred to as MLPs, it’s important to note that they are actually comprised of sigmoid neurons, not perceptrons, as most real-world problems are nonlinear. Data usually is fed into these models to train them, and they are the foundation for computer vision, natural language processing, and other neural networks.
Convolutional neural networks (CNNs) are similar to feedforward networks, but they’re usually utilized for image recognition, pattern recognition, and/or computer vision. These networks harness principles from linear algebra, particularly matrix multiplication, to identify patterns within an image.
Recurrent neural networks (RNNs) are identified by their feedback loops. These learning algorithms are primarily leveraged when using time-series data to make predictions about future outcomes, such as stock market predictions or sales forecasting.
Now that we have talked about Neural Networks and Deep Learning Systems, we can move forward and see how they differ from each other!

Definition A neural network is a model of neurons inspired by the human brain. It is made up of many neurons that at inter-connected with each other. Deep learning neural networks are distinguished from neural networks on the basis of their depth or number of hidden layers.

Feed Forward Neural Networks

Recurrent Neural Networks

Symmetrically Connected Neural Networks

Recursive Neural Networks

Unsupervised Pre-trained Networks

Convolutional Neural Networks



Connection and weights

Propagation function

Learning rate





Time & Accuracy

It generally takes less time to train them.

They have a lower accuracy than Deep Learning Systems

It generally takes more time to train them.

They have a higher accuracy than Deep Learning Systems

* Feedforward Neural Networks – This is the most common type of neural network architecture, with the first layer being the input layer and the last layer being the output layer. All middle layers are hidden layers.

* Recurrent Neural Network – This network architecture is a series of ANNs in which connections between nodes form a directed graph along a temporal sequence. Therefore, this type of network exhibits dynamic behavior over time.

* Symmetrically Connected Neural Networks – These are similar to recurrent neural networks, with the only difference being that the connections between units are symmetric in symmetrically connected neural networks (i.e. same weight n both directions).
A neural network has the following components
* Neurons – A neuron is a mathematical function that attempts to mimic the behavior of a biological neuron. It calculates the weighted average of the data supplied and then sends the data through a nonlinear function, called the logistic function.

* Connections and weights – Connections link a neuron in one layer to another neuron in the same layer or another layer, as the name implies. A weight value is assigned to each connection. The strength of the relationship between the units is represented by a weight. The goal is to lower the weight number in order to diminish the chances of losing weight (error).

* Propagation – In a Neural Network, there are two propagation functions: forward propagation, which produces the “predicted value,” and backward propagation, which delivers the “error value.”

* Learning Rate – Gradient Descent is used to train neural networks. At each iteration, the derivative of the loss function is calculated in reference to each weight value using back-propagation and then subtracted from that weight. The learning rate determines how quickly or slowly the weight values of the model are updated.
To understand the concept of the architecture of an artificial neural network, we have to understand what a neural network consists of. In order to define a neural network that consists of a large number of artificial neurons, which are termed units arranged in a sequence of layers. Lets us look at various types of layers available in an artificial neural network.
Artificial Neural Network primarily consists of three layers:

Input Layer : As the name suggests, it accepts inputs in several different formats provided by the programmer.
Hidden Layer : The hidden layer presents in-between input and output layers. It performs all the calculations to find hidden features and patterns.
Output Layer : The input goes through a series of transformations using the hidden layer, which finally results in output that is conveyed using this layer.
The artificial neural network takes input and computes the weighted sum of the inputs and includes a bias. This computation is represented in the form of a transfer function.
It determines weighted total is passed as an input to an activation function to produce the output. Activation functions choose whether a node should fire or not. Only those who are fired make it to the output layer. There are distinctive activation functions available that can be applied upon the sort of task we are performing.
Parallel processing capability : Artificial neural networks have a numerical value that can perform more than one task simultaneously.
Storing data on the entire network : Data that is used in traditional programming is stored on the whole network, not on a database. The disappearance of a couple of pieces of data in one place doesn't prevent the network from working.
Capability to work with incomplete knowledge : After ANN training, the information may produce output even with inadequate data. The loss of performance here relies upon the significance of missing data.
Having a memory distribution : For ANN is to be able to adapt, it is important to determine the examples and to encourage the network according to the desired output by demonstrating these examples to the network. The succession of the network is directly proportional to the chosen instances, and if the event can't appear to the network in all its aspects, it can produce false output.
Having fault tolerance : Extortion of one or more cells of ANN does not prohibit it from generating output, and this feature makes the network fault-tolerance.
Assurance of proper network structure :  There is no particular guideline for determining the structure of artificial neural networks. The appropriate network structure is accomplished through experience, trial, and error.
Unrecognized behavior of the network : It is the most significant issue of ANN. When ANN produces a testing solution, it does not provide insight concerning why and how. It decreases trust in the network.
Hardware dependence : Artificial neural networks need processors with parallel processing power, as per their structure. Therefore, the realization of the equipment is dependent.
Difficulty of showing the issue to the network : ANNs can work with numerical data. Problems must be converted into numerical values before being introduced to ANN. The presentation mechanism to be resolved here will directly impact the performance of the network. It relies on the user's abilities.
The duration of the network is unknown : The network is reduced to a specific value of the error, and this value does not give us optimum results.
* Artificial neural networks are modelled from biological neurons.
* The connections of the biological neuron are modeled as weights.
* A positive weight reflects an excitatory connection, while negative values mean inhibitory connections.
* All inputs are modified by a weight and summed. This activity is referred to as a linear combination.
* Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1.

Neural Networks modelled