Programming, AI, Machine Learning & Software Development Tutorials
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….
Training a machine learning model is, at its core, a search problem. Your model starts with random weights and tries to find a configuration that minimizes the loss function. Gradient descent provides the direction, but the learning rate determines how far the optimizer moves in that direction. And that creates a fundamental problem. Take steps that … Read more
Let’s switch to the California housing problem and tackle it using a regression neural network. For simplicity, we will use Scikit-Learn’s fetch_california_housing() function to load the data. This dataset is simpler since it contains only numerical features. There is no ocean_proximity feature, and there is no missing value. After loading the data, we split it … Read more
Warren McCulloch and Walter Pitts proposed a very simple model of the biological neuron, which later became known as an artificial neuron: it has one or more binary (on/off) inputs and one binary output. The artificial neuron simply activates its output when more than a certain number of its inputs are active. McCulloch and Pitts … Read more
Stochastic Gradient Descent (SGD) is an optimization algorithm commonly used in machine learning for training models, particularly in large-scale and online learning settings. It is an iterative optimization algorithm that aims to minimize a cost or loss function by adjusting the model parameters. Here’s an overview of how SGD works: Basic Concept: Advantages and Considerations: … Read more
Surprisingly, ANNs have been around for quite a while: they were first introduced back in 1943 by the neurophysiologist Warren McCulloch and the mathematician Walter Pitts. In their landmark paper, “A Logical Calculus of Ideas Immanent in Nervous Activity,”. McCulloch and Pitts presented a simplified computational model of how biological neurons might work together in … Read more
With thousands of parameters you can fit the whole zoo. Deep neural networks typically have tens of thousands of parameters, sometimes even millions. With so many parameters, the network has an incredible amount of freedom and can fit a huge variety of complex datasets. But this great flexibility also means that it is prone to … Read more
Introduction In the dynamic landscape of machine learning, Multilayer Perceptrons (MLPs) emerge as formidable tools capable of handling both regression and classification tasks with finesse. Whether you’re predicting housing prices or sorting emails, understanding how to tailor MLP architectures and activations is pivotal for optimizing performance. Regression MLPs Crafting an MLP architecture for regression tasks … Read more
An MLP is composed of one (passthrough) input layer, one or more layers of TLUs, called hidden layers, and one final layer of TLUs called the output layer (seeFigure below). The layers close to the input layer are usually called the lower layers, and the ones close to the outputs are usually called the upper … Read more
Another very popular Boosting algorithm is Gradient Boosting. Just like AdaBoost,Gradient Boosting works by sequentially adding predictors to an ensemble, each one correcting its predecessor. However, instead of tweaking the instance weights at every iteration like AdaBoost does, this method tries to fit the new predictor to the residual errors made by the previous predictor. … Read more
A. Introduction to neural networksB. ANN architectures C. Learning methods D. Learning rule on supervised learning E. Feedforward neural network with Gradient descent optimization Introduction to neural networks Definition: the ability to learn, memorize and still generalize, prompted research in algorithmic modeling of biological neural systems. Human brain has the ability to perform tasks such … Read more