Posts tagged "FNNs"

NOTE: This post is part of my Machine Learning Series where I’m discussing how AI/ML works and how it has evolved over the last few decades.

Feedforward Neural Networks (FNNs), also known as Multi-Layer Perceptrons (MLPs), are one of the most fundamental and widely-used neural network architectures in machine learning. FNNs have been employed for a variety of tasks, including classification, regression, and feature extraction. In this post, we'll explore the architecture, training process, and applications of FNNs.

Architecture of FNNs

An FNN consists of multiple layers of neurons, including an input layer, one or more hidden layers, and an output layer. Each neuron is connected to the neurons in the adjacent layers through weighted edges.

Neurons: Building Blocks of FNNs

A neuron receives inputs from other neurons or external sources, applies an activation function, and produces an output. Common activation functions include the sigmoid, ReLU (Rectified Linear Unit), and tanh functions.

Activation Functions in Neural Networks

Layers: Input, Hidden, and Output

• Input Layer: The input layer receives raw data features and passes them to the hidden layers.
• Hidden Layer(s): Hidden layers perform complex transformations on the data using weighted connections and activation functions.
• Output Layer: The output layer provides the final predictions or classifications.

Training FNNs: Backpropagation and Gradient Descent

Training FNNs involves adjusting the weights and biases to minimize the loss function. The loss function measures the difference between the predicted output and the actual target.

Backpropagation

Backpropagation is an algorithm used to calculate the gradients of the loss function with respect to the weights and biases. It uses the chain rule to efficiently propagate error signals from the output layer to the input layer.

Backpropagation Explained

Gradient Descent

Gradient descent is an optimization algorithm that updates the weights and biases based on the gradients calculated during backpropagation. Variants like stochastic gradient descent (SGD) and Adam optimizer improve the optimization process.

Gradient Descent: The Optimization Algorithm

Applications of FNNs

• Classification: FNNs can classify data into distinct categories, such as spam or not-spam for email filtering.
• Regression: FNNs can predict continuous values, such as house prices based on property features.

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NOTE: This post is part of my Machine Learning Series where I’m discussing how AI/ML works and how it has evolved over the last few decades.

Neural networks are the foundation of many artificial intelligence and machine learning applications. There are several types of neural networks, each designed to address specific types of problems. In this post, we'll explore the most common types of neural networks and their applications.

Feedforward Neural Networks (FNNs)

Feedforward neural networks, also known as FNNs, are the simplest type of neural network. They consist of an input layer, one or more hidden layers, and an output layer. Information in FNNs flows in one direction, from the input to the output.

 Understanding Feed Forward Neural Networks With Maths and Statistics 

Convolutional Neural Networks (CNNs)

Convolutional neural networks (CNNs) are designed for image processing and computer vision tasks. CNNs use convolutional layers to scan images for local patterns, and pooling layers to reduce spatial dimensions. They excel at image classification and object detection.

A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way

Recurrent Neural Networks (RNNs)

Recurrent neural networks (RNNs) are designed to process sequential data, such as time series or text. RNNs have connections that loop back, allowing them to capture temporal dependencies. Variants such as LSTMs and GRUs address challenges like vanishing gradients.

Understanding RNN and LSTM

Generative Adversarial Networks (GANs)

Generative adversarial networks (GANs) consist of a generator and discriminator network that engage in an adversarial game. The generator creates synthetic data, while the discriminator evaluates its authenticity. GANs have applications in image synthesis and data augmentation.

 Understanding Generative Adversarial Networks (GANs)

Autoencoders

Autoencoders are neural networks used for dimensionality reduction and feature extraction. They consist of an encoder that compresses input data and a decoder that reconstructs the original data. Autoencoders are used for image denoising and anomaly detection.

 Applied Deep Learning - Part 3: Autoencoders

TL;DR