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Directed Networks

Regularization -- Part 2


These are the lecture notes for FAU's YouTube Lecture "Deep Learning". This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. If you spot mistakes, please let us know!

Causal AI & Bayesian Networks


We are all familiar with the dictum that "correlation does not imply causation". Furthermore, given a data file with samples of two variables x and z, we all know how to calculate the correlation between x and z. But it's only an elite minority, the few, the proud, the Bayesian Network aficionados, that know how to calculate the causal connection between x and z. Neural Net aficionados are incapable of doing this. Their Neural nets are just too wimpy to cut it.

Generative Adversarial Networks (GANs) & Bayesian Networks


Generative Adversarial Networks (GANs) software is software for producing forgeries and imitations of data (aka synthetic data, fake data). Human beings have been making fakes, with good or evil intent, of almost everything they possibly can, since the beginning of the human race. Thus, perhaps not too surprisingly, GAN software has been widely used since it was first proposed in this amazingly recent 2014 paper. To gauge how widely GAN software has been used so far, see, for example, this 2019 article entitled "18 Impressive Applications of Generative Adversarial Networks (GANs)" Sounds (voices, music,...), Images (realistic pictures, paintings, drawings, handwriting, ...), Text,etc. The forgeries can be tweaked so that they range from being very similar to the originals, to being whimsical exaggerations thereof.

Bayes' Theorem in Layman's Terms


If you have difficulty in understanding Bayes' theorem, trust me you are not alone. In this tutorial, I'll help you to cross that bridge step by step. Let's consider Alex and Brenda are two people in your office, When you are working you saw someone walked in front of you, and you didn't notice who is she/he. Now I'll give you extra information, Let's calculate the probabilities with this new information, Probability that Alex is the person passed by is 2/5 i.e, Probability that Brenda is the person passed by is 3/5 i.e, Probabilities that we are calculated before the new information are called Prior, and probabilities that we are calculated after the new information are called Posterior. Consider a scenario where, Alex comes to the office 3 days a week, and Brenda comes to the office 1 day a week.

Maximum likelihood estimation for Machine Learning - Nucleusbox


In the Logistic Regression for Machine Learning using Python blog, I have introduced the basic idea of the logistic function. We have discussed the cost function. And in the iterative method, we focus on the Gradient descent optimization method. Now so in this section, we are going to introduce the Maximum Likelihood cost function. And we would like to maximize this cost function.

Top 12 R Packages For Machine Learning In 2020


R is one of the most prevalent programming languages for statistical analysis and computing. Researchers in the field of data science and statistical computing have been using this language for a few years now because of its number of intuitive features. These features include running code without a compiler, open-source, robust visualisation library, and other such. This article lists down the top 12 R packages for machine learning one must know in 2020. About: The Classification And REgression Training or caret package is a set of functions that seeks to streamline the method for creating predictive models.

Identifying light sources using machine learning


The identification of light sources is very important for the development of photonic technologies such as light detection and ranging (LiDAR), and microscopy. Typically, a large number of measurements are needed to classify light sources such as sunlight, laser radiation, and molecule fluorescence. The identification has required collection of photon statistics or quantum state tomography. In recently published work, researchers have used a neural network to dramatically reduce the number of measurements required to discriminate thermal light from coherent light at the single-photon level. In their paper, authors from Louisiana State University, Universidad Nacional Autónoma de México and Max-Born-Institut describe their experimental and theoretical techniques.

Linear Discriminant Analysis for Dimensionality Reduction in Python


Reducing the number of input variables for a predictive model is referred to as dimensionality reduction. Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data. Linear Discriminant Analysis, or LDA for short, is a predictive modeling algorithm for multi-class classification. It can also be used as a dimensionality reduction technique, providing a projection of a training dataset that best separates the examples by their assigned class. The ability to use Linear Discriminant Analysis for dimensionality reduction often surprises most practitioners.

Predicting Diabetes Using a Machine learning Approach


Using the ML approach, we can now assess diabetes in the patient. Learn more about how the algorithms used are dramatically changing health care. Diabetes is one of the deadliest diseases in the world. It is not only a disease, but also a creator of a variety of diseases such as heart attacks, blindness, and kidney diseases. The usual detection process is that patients visit the diagnostic center, consult their physician, and sit tight for a day or more to get their reports.