The concept of a recently proposed Forward-Forward learning algorithm for fully connected artificial neural networks is applied to a single multi output perceptron for classification. The parameters of the system are trained with respect to increased (decreased) "goodness" for correctly (incorrectly) labelled input samples. Basic numerical tests demonstrate that the trained perceptron effectively deals with data sets that have non-linear decision boundaries. Moreover, the overall performance is comparable to more complex neural networks with hidden layers. The benefit of the approach presented here is that it only involves a single matrix multiplication.

Decision Tree Classification: This type divides a dataset into segments based on particular feature variables. The divisions' threshold values are typically the mean or mode of the feature variable in question if they happen to be numerical. K-Nearest Neighbors: This Classification type identifies the K nearest neighbors to a given observation point. It then uses K points to evaluate the proportions of each type of target variable and predicts the target variable that has the highest ratio. Logistic Regression: This classification type isn't complex so it can be easily adopted with minimal training. It predicts the probability of Y being associated with the X input variable.

Support vector machines (SVM) is a supervised machine learning technique. And, even though it's mostly used in classification, it can also be applied to regression problems. SVMs define a decision boundary along with a maximal margin that separates almost all the points into two classes. Support vector machines are an improvement over maximal margin algorithms. Its biggest advantage is that it can define both a linear or a non-linear decision boundary by using kernel functions.

Welcome back to my second post of the series Deep Learning for Rookies (DLFR), by yours truly, a rookie;) Feel free to refer back to my first post here or my blog if you find it hard to follow. Or highlight on this page with notes or leave a comment below! Your feedback will be highly appreciated, too. We will go deeper into neural networks this time and the post will be slightly more technical than last time. But no worries, I will make it as easy and intuitive as possible for you to learn the basics without CS/Math background.

The additional sparse features can be incorporated into the linear model, similar to kernel methods used in machine learning. As mentioned, these solutions have now allowed a non-linear decision boundary in conjunction with a linear classifier. In other words, since a straight line could not accurately represent the distribution of our data, we are now able to accurately represent the distribution of the two classes with a non-linear decision boundary, which make take the form of a curved line or multiple lines. But we're representing that non-linear boundary with a linear classifier so that our results will be interpretable.

Welcome back to my second post of the series Deep Learning for Rookies (DLFR), by yours truly, a rookie;) Feel free to refer back to my first post here or my blog if you find it hard to follow. Or highlight on this page with notes or leave a comment below! Your feedback will be highly appreciated, too. We will go deeper into neural networks this time and the post will be slightly more technical than last time. But no worries, I will make it as easy and intuitive as possible for you to learn the basics without CS/Math background.

Discussion[Discussion] How Gaussian naïve Bayes forms a non-linear decision boundary? Also, please explain decision boundary for decision trees. If the two gaussians are non isotropic you can derive that the bound is quadratic/elliptic curve.