Supervised Learning - Classification Week 9 Challenge
1. What is a Neural Network?
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.
2. What is deep learning?
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.
3. What is the role of the activation function?
An Activation Function decides whether a neuron should be activated or not. This means that it will decide whether the neuron's input to the network is important or not in the process of prediction using simpler mathematical operations. And activation functions provide the building blocks that can be used repeatedly in two dimensions of the network structure so that, combined with an attenuation matrix to vary the weight of signaling from layer to layer, is known to be able to approximate an arbitrary and complex function.
4. What are the different types of activation functions?
There are perhaps three most imortant activation functions you may want to consider for use in hidden layers; they are: Rectified Linear Activation (ReLU) Logistic (Sigmoid) Hyperbolic Tangent (Tanh). SOme other popular types of activation functions and when to use them:
Binary Step
Linear
Leaky ReLU
Parameterised ReLU
Exponential Linear Unit
Swish
Softmax
5. What do you understand by backpropagation?
Backpropagation (backward propagation) is an important mathematical tool for improving the accuracy of predictions in data mining and machine learning. Essentially, backpropagation is an algorithm used to calculate derivatives quickly.
Artificial neural networks use backpropagation as a learning algorithm to compute a gradient descent with respect to weights. Desired outputs are compared to achieved system outputs, and then the systems are tuned by adjusting connection weights to narrow the difference between the two as much as possible. The algorithm gets its name because the weights are updated backwards, from output towards input.
The difficulty of understanding exactly how changing weights and biases affects the overall behavior of an artificial neural network was one factor that held back wider application of neural network applications, arguably until the early 2000s when computers provided the necessary insight. Today, backpropagation algorithms have practical applications in many areas of artificial intelligence (AI), including optical character recognition (OCR), natural language processing (NLP) and image processing.
Because backpropagation requires a known, desired output for each input value in order to calculate the loss function gradient, it is usually classified as a type of supervised machine learning. Along with classifiers such as Naïve Bayesian filters and decision trees, the backpropagation algorithm has emerged as an important part of machine learning applications that involve predictive analytics.