The course will start off at the basics and work all the way through the financial accounting topics generally covered in an undergraduate program. First, we will describe what financial accounting is and the objectives of financial accounting. We will learn how the double-entry accounting system works by applying it to the accounting equation. In other words, we will use an accounting equation to record financial transactions using a double-entry accounting system. We well learn all topics by fist having presentations and then applying the skills using Excel practice problems.

Mastering How to build, work with, handle and manage databases can be a very useful skill for any programmer, developer and also, non-programmers. And by the end of this course you'll be able to build, work with and manage databases easily and in no time! This is a very powerful tool for everyone who wants to become a professional developer, engineer or data scientist. Structure Query Language(SQL) is one of the most important skills that any programmer, developer, engineer or even an expert must possess, who wants to succeed in his practical life. In this immersive training you'll learn everything you need to program with the SQL language the right way, and you'll become a SQL Programming Rockstar.

In the fall of 2015, Rob Reich, a philosopher and a political scientist at Stanford, was chatting with a freshman during office hours. "I asked him what he planned to study," Reich recalled recently. "He said, 'Definitely computer science. I have some ideas for startups.' " In the spirit of small talk, Reich asked, What kind? "He looked at me with total earnestness and said, 'To tell you that, I'd have to ask you to sign a nondisclosure agreement.'

Ryan Ahmed is a best-selling Udemy instructor who is passionate about education and technology. Ryan's mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master's of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada.

Welcome to the Ethics of AI! The Ethics of AI is a free online course created by the University of Helsinki. The course is for anyone who is interested in the ethical aspects of AI – we want to encourage people to learn what AI ethics means, what can and can't be done to develop AI in an ethically sustainable way, and how to start thinking about AI from an ethical point of view.

The attention mechanism was introduced to improve the performance of the encoder-decoder model for machine translation. The idea behind the attention mechanism was to permit the decoder to utilize the most relevant parts of the input sequence in a flexible manner, by a weighted combination of all of the encoded input vectors, with the most relevant vectors being attributed the highest weights. In this tutorial, you will discover the attention mechanism and its implementation. The Attention Mechanism from Scratch Photo by Nitish Meena, some rights reserved. The attention mechanism was introduced by Bahdanau et al. (2014), to address the bottleneck problem that arises with the use of a fixed-length encoding vector, where the decoder would have limited access to the information provided by the input. This is thought to become especially problematic for long and/or complex sequences, where the dimensionality of their representation would be forced to be the same as for shorter or simpler sequences.

This is the Qiskit s machine learning module. There is an initial set of function here that will be built out over time. At present it has sample sets that can be used with classifiers and circuits used in machine learning applications. Class for errors returned by Qiskit's machine learning module. A neural network is a parametrized network which may be defined as a artificial neural network - classical neural network - or as parametrized quantum circuits - quantum neural network.

A common challenge for teams working on video machine learning applications is how to scale and automate their ML lifecycle when working with these types of large unstructured datasets. In this latest Data Science Central webinar, Vincent Koops, Senior Data Scientist at RTL Netherlands, will walk through their Video AI platform at RTL and how they've addressed these challenges. Their platform is built on top of Pachyderm and Kubernetes to enable a wide range of ML applications such as automatic thumbnail picking and mid-roll marking.

Welcome to Google Calendar: and help you if you are a virtual assistant I'll be your calendar coach! Learn everything you need to know about Google Calendar with this complete guide! Managing your own appointments and calendars can be daunting and stressful if you are working as a virtual assistant. Then you've come to the right place! Whether you are a complete beginner or just looking to refresh your existing knowledge of Google Calendar, this is the course for you.

As student failure rates continue to increase in higher education, predicting student performance in the following semester has become a significant demand. Personalized student performance prediction helps educators gain a comprehensive view of student status and effectively intervene in advance. However, existing works scarcely consider the explainability of student performance prediction, which educators are most concerned about. In this paper, we propose a novel Explainable Student performance prediction method with Personalized Attention (ESPA) by utilizing relationships in student profiles and prior knowledge of related courses. The designed Bidirectional Long Short-Term Memory (BiLSTM) architecture extracts the semantic information in the paths with specific patterns. As for leveraging similar paths' internal relations, a local and global-level attention mechanism is proposed to distinguish the influence of different students or courses for making predictions. Hence, valid reasoning on paths can be applied to predict the performance of students. The ESPA consistently outperforms the other state-of-the-art models for student performance prediction, and the results are intuitively explainable. This work can help educators better understand the different impacts of behavior on students' studies.