Building a Regression MLP Using the Sequential API
Let’s switch to the California housing problem and tackle it using a regression neural network. For simplicity, we will use…
Category: Deep Learning
The definition of Deep learning is that it is the branch of machine learning that is based on artificial neural network architecture. An artificial neural network or ANN uses layers of interconnected nodes called n….
Logical Computations with Neurons | Deep learning
Warren McCulloch and Walter Pitts proposed a very simple model of the biological neuron, which later became known as an…
What is Stochastic Gradient Descent?
Stochastic Gradient Descent (SGD) is an optimization algorithm commonly used in machine learning for training models, particularly in large-scale and…
A Journey From Biological to Artificial Neurons
Surprisingly, ANNs have been around for quite a while: they were first introduced back in 1943 by the neurophysiologist Warren…
Avoiding Overfitting Through Regularization
With thousands of parameters you can fit the whole zoo. Deep neural networks typically have tens of thousands of parameters,…
Regression and Classification Multi Layer Perceptrons
Introduction In the dynamic landscape of machine learning, Multilayer Perceptrons (MLPs) emerge as formidable tools capable of handling both regression…
Multi-Layer Perceptron and Backpropagation | Deep Learning
An MLP is composed of one (passthrough) input layer, one or more layers of TLUs, called hidden layers, and one…
Gradient Boosting in Machine Learning
Another very popular Boosting algorithm is Gradient Boosting. Just like AdaBoost,Gradient Boosting works by sequentially adding predictors to an ensemble,…
What is Artificial Neural Network (ANN)?
A. Introduction to neural networksB. ANN architectures C. Learning methods D. Learning rule on supervised learning E. Feedforward neural network…