– What is a Perceptron? What is the role of bias in a perceptron (or neuron)?
– What is Deep Learning? Discuss its key characteristics, working and applications
– Explain the basic architecture and training process of a Neural Network model
A Multilayer Perceptron (MLP) is one of the simplest and most common neural network architectures used in machine learning. It is a feedforward artificial neural network consisting of multiple layers of interconnected neurons, including an input layer, one or more hidden layers, and an output layer. In the context of Deep Learning, a Perceptron is usually referred to as a neuron, and a Multi- Layer Perceptron structure is referred to as a Neural Network.
Key characteristics of a Multilayer Perceptron (MLP)
- Feedforward Architecture
In an MLP, information flows in one direction, from the input layer through the hidden layers to the output layer. There are no feedback loops or recurrent connections
- Neurons and Layers
Each layer consists of multiple neurons (also known as nodes or units), and the layers are fully connected, meaning each neuron in one layer is connected to every neuron in the adjacent layers
- Activation Functions
Neurons within an MLP typically use non-linear activation functions (e.g., ReLU, sigmoid, or tanh) to introduce non-linearity into the model, allowing it to learn complex relationships in the data
- Weighted Connections
Connections between neurons have associated weights, which are learned during the training process. These weights determine the strength of the connections and play a crucial role in the networkʼs ability to capture patterns in the data
Each neuron usually has an associated bias term, which allows for fine-tuning and shifting the activation functionʼs threshold
MLPs are capable of learning complex and non-linear relationships in data, especially when they have multiple hidden layers and non-linear activation functions (see pic below).
MLPs are trained using a process called backpropagation, which involves adjusting the weights and biases of the neurons to minimize the difference between the predicted output and the actual output, also referred to as loss. Backpropagation is typically done using optimization algorithms, such as stochastic gradient descent (SGD).
Applications of Multilayer perceptron
MLPs are universal function approximators, i.e. they are capable of approximating any continuous function to a desired level of accuracy, given enough hidden neurons and appropriate training. This property makes them powerful tools for solving a wide range of problems including:
- Classication such as sentiment analysis, fraud detection
- Regression such as score estimation
- NLP tasks such as machine translation
- Anomaly Detection
- Speech Recognition in virtual assistant systems such as Siri, Alexa
- Computer Vision for object identification, image segmentation
- Data analytics and data visualization
- In this set of two videos (Runtime: 18 minutes each) by Coding Train, Daniel Shiman provides a good build up of why we need Multilayer perceptron (consisting of multiple neurons and layers) as compared to a single perceptron (a neuron) to solve complex non-linear problems. You may choose to watch just the first video to gain a good understanding or continue with the second part for a deeper insight.