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A Gentle Introduction to Multivariate Calculus

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It is often desirable to study functions that depend on many variables. Multivariate calculus provides us with the tools to do so by extending the concepts that we find in calculus, such as the computation of the rate of change, to multiple variables. It plays an essential role in the process of training a neural network, where the gradient is used extensively to update the model parameters. In this tutorial, you will discover a gentle introduction to multivariate calculus. A Gentle Introduction to Multivariate Calculus Photo by Luca Bravo, some rights reserved.


Linear Regression in Python

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Forecasting in general means to display, where this exactly is to display or predict future trends using previous or historical data as inputs to obtain an efficient and effective estimation from the predictive data. Forecasting models have different methods for different situations and evaluation procedures are also conducted. Forecasting evaluation includes a procedure to be carried out in step by step that starts with testing of assumptions, testing data and methods, replicating outputs, and accessing outputs. There are three different types of forecasting which basic types of forecasting are: qualitative techniques, time series analysis and projection, and casual models. In this course you will be introduced to Linear Regression in Python, Importing Libraries, Graphical Univariate Analysis, Boxplot, Linear Regression Boxplot, Linear Regression Outliers, Bivariate Analysis, Machine Learning Base Run and Predicting Output.


Applications of Derivatives

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The derivative defines the rate at which one variable changes with respect to another. It is an important concept that comes in extremely useful in many applications: in everyday life, the derivative can tell you at which speed you are driving, or help you predict fluctuations on the stock market; in machine learning, derivatives are important for function optimization. This tutorial will explore different applications of derivatives, starting with the more familiar ones before moving to machine learning. We will be taking a closer look at what the derivatives tell us about the different functions we are studying. In this tutorial, you will discover different applications of derivatives.


Mastering JavaScript Essentials 2021 Novice To Professional

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Have you always wanted to learn JavaScript but you just don't know where to start? Or maybe you have started to learn Javascript, but you just don't know how to work with basic concepts like the intermediate level JavaScript programming, object-oriented programming in JavaScript, asynchronous programming in JavaScript and JSON objects. If that Sounds Like you…. Then our complete Mastering JavaScript Essentials 2021 Novice to Professional is for You! Join 800,000 Students Who Have Enrolled in our Udemy Courses! Watch the Promo Video to see how you can Get Started Today!


Optimization Algorithm Using Matlab

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I'm very glad to have opportunity to teach you one of the most popular and powerful optimization algorithms in this course. If you search FireFly optimization algorithm in google scholar, it could be seen that there are many vast range of papers has been published by implementing this optimization algorithm in different fields of science. In this course, after presenting the mathematical concept of each part of the considered optimization algorithm, I write its code immediately in matlab. All of the written codes are available, however, I strongly suggest to write the codes with me. Notice that, if you don't have matlab or you know another programming language, don't worry at all.


861 candidates complete online training in cyber security, AI & ML

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… 12-week-long “Online Training and Certification Course (OTCC) in Cyber Security, Artificial Intelligence and Machine Learning (AI & ML)” conducted …


Best Deep Learning Courses

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If you want to become an expert in machine learning, you must also learn deep learning. There are many paid and free courses on the internet that can give you a comprehensive knowledge of the concepts of deep learning. So, if you want to know about the best deep learning courses, this article is for you. In this article, I'm going to introduce you to some of the best deep learning courses you can choose for learning deep learning. I found and selected two deep learning courses on the Internet.


ggforce: Make a Hull Plot to Visualize Clusters in ggplot2

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The ggforce package is a ggplot2 extension that adds many exploratory data analysis features. In this tutorial, we'll learn how to make hull plots for visualizing clusters or groups within our data. This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks. Here are the links to get set up. Learn how to use ggforce in our 7-minute YouTube video tutorial. The Hull Plot is a visualization that produces a shaded areas around clusters (groups) within our data.


Day 23: BST Level-Order Traversal

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Check out the Tutorial tab for learning materials and an instructional video! Task A level-order traversal, also known as a breadth-first search, visits each level of a tree's nodes from left to right, top to bottom. You are given a pointer,, pointing to the root of a binary search tree. Complete the levelOrder function provided in your editor so that it prints the level-order traversal of the binary search tree. Hint: You'll find a queue helpful in completing this challenge.


DP-100 Azure Machine Learning In Python-Basic To Advance

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This course has been designed keeping in mind entry level Data Scientists or no background in programming. This course will also help the data scientists and python developers to learn the AzureML . This course is designed based on latest changes done in DP-100 Certification. This course would also be useful for the experts who needs to know how to create and deploy a machine learning environment in production. Will train machine learning and deep learning algorithm in azure ml in local machine and same code will be executed in azure as well.