The vector notation adopted by GNU Octave plays a significant role as a tool for introspection, aligning itself with the vision of Kenneth E. Iverson. He believed that, just like mathematics, a programming language should be an effective thinking tool for representing and reasoning about problems we wish to address. This work aims to explore the use of vector notation in GNU Octave through the analysis of operators and functions, providing a closer alignment with mathematical notation and enhancing code efficiency. We will delve into fundamental concepts such as indexing, broadcasting, and function handles, and present case studies for a deeper understanding of these concepts. By adopting vector notation, GNU Octave becomes a powerful tool for mathematicians, scientists and engineers, enabling them to express and solve complex problems more effectively and intuitively.

The new buzzword shaking the global business arena is machine learning. It's grabbed the public's imagination, conjuring up images of self-learning AI and robots in the future. Machine learning has prepared the path for technical advancements and tools in manufacturing that would have been unthinkable just a few years ago. It drives the breakthrough technologies that sustain our ways of living, from prediction machines to online TV live streaming. If words like deep learning, neural learning, and artificial intelligence spark your interest, we have a great list of free machine learning courses you can begin with right now.

These kinds of mysteries used to require a lot of foresight and engineering work to deal with. But now, Comcast says it can use artificial intelligence to solve similar problems automatically. Prompted by the coronavirus pandemic, the company developed an AI system called Octave that can detect network anomalies and figure out how to address them. "It's not just automating what smart engineers can do. It's going to places where they just couldn't process that amount of information and come up with solutions quick enough to do what [Octave] does," says Tony Werner, Comcast's president of technology, product, and "Xperience."

In most fuzzy control applications (applying classical fuzzy reasoning), the reasoning method requires a complete fuzzy rule-base, i.e all the possible observations must be covered by the antecedents of the fuzzy rules, which is not always available. Fuzzy control systems based on the Fuzzy Rule Interpolation (FRI) concept play a major role in different platforms, in case if only a sparse fuzzy rule-base is available. This cases the fuzzy model contains only the most relevant rules, without covering all the antecedent universes. The first FRI toolbox being able to handle different FRI methods was developed by Johanyak et. al. in 2006 for the MATLAB environment. The goal of this paper is to introduce some details of the adaptation of the FRI toolbox to support the GNU/OCTAVE programming language. The OCTAVE Fuzzy Rule Interpolation (OCTFRI) Toolbox is an open-source toolbox for OCTAVE programming language, providing a large functionally compatible subset of the MATLAB FRI toolbox as well as many extensions. The OCTFRI Toolbox includes functions that enable the user to evaluate Fuzzy Inference Systems (FISs) from the command line and from OCTAVE scripts, read/write FISs and OBS to/from files, and produce a graphical visualisation of both the membership functions and the FIS outputs. Future work will focus on implementing advanced fuzzy inference techniques and GUI tools.

If I could go back in time, I would try myself in Machine Learning 12 years ago! Right when I finished undergrad and came to the USA. After starting Andrew Ng's Machine Learning course on Coursera last month, I dropped everything except most urgent things and completed an 11 week course in just 3 weeks. The somewhat sad truth is, I first enrolled in this course many months ago, but I didn't start it then. Stars finally aligned in August and I started that course.

TL;DR - You can take the ML course on Coursera and you're magically a data scientist, because three really intelligent people did it. I'm not claiming the people referenced in this article are not data scientists who score high in Kaggle competitions. They're probably really intelligent people who picked up a new skill and excelled at it (although one was already an actuary, so he is basically doing machine learning in some form already). Here is my problem with it - being a data scientist usually requires a much larger skill set than a basic understanding of a few learning algorithms. I'm taking the Coursera ML course right now, and I think it is great!

"Today you are You, that is truer than true. There is no one alive who is Youer than You." "Explanations exist; they have existed for all time; there is always a well-known solution to every human problem -- neat, plausible, and wrong." In this 6th instalment of'Deep Learning from first principles in Python, R and Octave-Part6', I look at a couple of different initialization techniques used in Deep Learning, L2 regularization and the'dropout' method. Specifically, I implement "He initialization" & "Xavier Initialization". The implementation was in vectorized Python, R and Octave 3. Part 3 -In part 3, I derive the equations and also implement a L-Layer Deep Learning network with either the relu, tanh or sigmoid activation function in Python, R and Octave.

During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests. The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data. I learned to trust my logic when doing machine learning. If tweaking a parameter or engineering a new feature seems like a good idea logically, it's quite likely that it actually will help.

In this article, the problem of learning word representations with neural network from scratch is going to be described. This problem appeared as an assignment in the Coursera course Neural Networks for Machine Learning, taught by Prof. Geoffrey Hinton from the University of Toronto in 2012. In this article we will design a neural net language model. The model will learn to predict the next word given the previous three words.

In this 5th part on Deep Learning from first Principles in Python, R and Octave, I solve the MNIST data set of handwritten digits (shown below), from the basics. To do this, I construct a L-Layer, vectorized Deep Learning implementation in Python, R and Octave from scratch and classify the MNIST data set. The MNIST training data set contains 60000 handwritten digits from 0-9, and a test set of 10000 digits. MNIST, is a popular dataset for running Deep Learning tests, and has been rightfully termed as the'drosophila' of Deep Learning, by none other than the venerable Prof Geoffrey Hinton. The'Deep Learning from first principles in Python, R and Octave' series, so far included Part 1, where I had implemented logistic regression as a simple Neural Network. Part 2 implemented the most elementary neural network with 1 hidden layer, but with any number of activation units in that layer, and a sigmoid activation at the output layer.