Programming, AI, Machine Learning & Software Development Tutorials
An Artificial Neural Network (ANN) or simply a Neural Network (NN) is interconnected layers of small units called nodes that perform mathematical operations to detect patterns in data.
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
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