Supervised Learning - Classification Week 9 Challenge
Q1) What is Neural Network?
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 layer, 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 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 manual identification by human experts. One of the most well-known neural networks is Google’s search algorithm.
Q2) What is deep learning?
Deep learning is a type of machine learning and artificial intelligence that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial in collecting, analyzing, and interpreting large amounts of data. Deep learning makes this process faster and easier.
At its simplest, deep learning can be thought of as a way to automate predictive analytics. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.
Q3) What is the role of the activation function?
An activation function in a neural network defines how the weighted sum of the input is transformed into an output from a node or nodes in a layer of the network. Sometimes the activation function is called a “transfer function.” If the output range of the activation function is limited, then it may be called a “squashing function.” Many activation functions are nonlinear and may be referred to as “nonlinearity” in the layer or the network design. The choice of activation function has a large impact on the capability and performance of the neural network, and different activation functions may be used in different parts of the model. Technically, the activation function is used within or after the internal processing of each node in the network, although networks are designed to use the same activation function for all nodes in a layer.
Q4) What are the different types of activation functions?
The different types of activation functions used are:
- Binary Step
- Linear
- Sigmoid
- Tanh
- ReLU
- Leaky ReLU
- Parameterised ReLU
- Exponential Linear Unit
- Swish
- Softmax
Q5) What do you understand by backpropagation?
Backpropagation is an algorithm used in artificial intelligence to fine-tune mathematical weight functions and improve the accuracy of neural network outputs. A neural network can be thought of as a group of connected input/output nodes. The level of accuracy each node produces is expressed as a loss function (error rate). Backpropagation calculates the mathematical gradient of a loss function with respect to the other weights in the neural network. The calculations are then used to give artificial network nodes with high error rates less weight than nodes with lower error rates. Backpropagation uses a methodology called chain rule to improve outputs. Basically, after each forward pass through a network, the algorithm performs a backward pass to adjust the model’s weights. An important goal of backpropagation is to give data scientists insight into how changing a weight function will change loss functions and the overall behavior of the neural network. The term is sometimes used as a synonym for "error correction."