LaTeX and Beamer are great tools when preparing slides which contain mathematical formulas, circuit diagrams and that kind of stuff. However, to eliminate some nasty characteristics, few simple tricks are necessary.
Introduction to latex. Write your first document in latex
To Download latex for windows (163 MB)
http://miktex.org/download
To Download texmaker editor (53 MB)
http://www.xm1math.net/texmaker/download.html#windows
Here you can find a good latex templates
http://www.latextemplates.com/
https://www.sharelatex.com/templates/
https://www.writelatex.com/templates
Try sharelatex or writelatex for online editing
https://www.sharelatex.com/
https://www.writelatex.com/
Introduction to latex. Write your first document in latex
To Download latex for windows (163 MB)
http://miktex.org/download
To Download texmaker editor (53 MB)
http://www.xm1math.net/texmaker/download.html#windows
Here you can find a good latex templates
http://www.latextemplates.com/
https://www.sharelatex.com/templates/
https://www.writelatex.com/templates
Try sharelatex or writelatex for online editing
https://www.sharelatex.com/
https://www.writelatex.com/
Explainable AI - making ML and DL models more interpretableAditya Bhattacharya
Abstract –
Although industries have started to adopt AI and Machine Learning in almost every sector to solve complex business problems, but are these models always trustworthy? Machine Learning models are not any oracle but rather are scientific methods and mathematical models which best describes the data. But science is all about explaining complex natural phenomena in the simplest way possible! So, can we make ML and DL models more interpretable, so that any business user can understand these models and trust the results of these models?
In order to find out the answer, please join me in this session, in which I will take about concepts of Explainable AI and discuss its necessity and principles which help us demystify black-box AI models. I will be discussing about popular approaches like Feature Importance, Key Influencers, Decomposition trees used in classical Machine Learning interpretable. We will discuss about various techniques used for Deep Learning model interpretations like Saliency Maps, Grad-CAMs, Visual Attention Maps and finally go through more details about frameworks like LIME, SHAP, ELI5, SKATER, TCAV which helps us to make Machine Learning and Deep Learning models more interpretable, trustworthy and useful!
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Begin...Simplilearn
This K-Nearest Neighbor Classification Algorithm presentation (KNN Algorithm) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into these slides to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Learn more at: https://www.simplilearn.com
Explainable AI - making ML and DL models more interpretableAditya Bhattacharya
Abstract –
Although industries have started to adopt AI and Machine Learning in almost every sector to solve complex business problems, but are these models always trustworthy? Machine Learning models are not any oracle but rather are scientific methods and mathematical models which best describes the data. But science is all about explaining complex natural phenomena in the simplest way possible! So, can we make ML and DL models more interpretable, so that any business user can understand these models and trust the results of these models?
In order to find out the answer, please join me in this session, in which I will take about concepts of Explainable AI and discuss its necessity and principles which help us demystify black-box AI models. I will be discussing about popular approaches like Feature Importance, Key Influencers, Decomposition trees used in classical Machine Learning interpretable. We will discuss about various techniques used for Deep Learning model interpretations like Saliency Maps, Grad-CAMs, Visual Attention Maps and finally go through more details about frameworks like LIME, SHAP, ELI5, SKATER, TCAV which helps us to make Machine Learning and Deep Learning models more interpretable, trustworthy and useful!
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Begin...Simplilearn
This K-Nearest Neighbor Classification Algorithm presentation (KNN Algorithm) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into these slides to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Learn more at: https://www.simplilearn.com
Getting started with Website Project and Sublime Text 2Amanda Zimmer
This Power Point Presentation explains step by step with screenshots how to create folders and sub-folders on Windows PC. The second part of this presentation shows users how to download, install, use and customize the text editor "Sublime Text 2," which is a program students will be using in my HTML5 and CSS3 introductory course.
13 - Panorama Necto 14 building models - visualization & data discovery solu...Panorama Software
PANORAMA NECTO 14 TRAINING - Panorama is leading a Business Intelligence 3.0 revolution and a creation of a new generation of Business Intelligence & Data Discovery solutions that enable organizations to leverage the power of Social Decision Making and Automated Intelligence to gain insights more quickly, more efficiently, and with greater relevancy.
www.panorama.com
Originally given at JoomlaDay Florida 2018 on many of my favorite CSS and Sass concepts. Covering things like CSS Layout Grid, Flexbox, and how to start using Element Queries.
Structuring your CSS for maintainability: rules and guile lines to write CSSSanjoy Kr. Paul
Structuring your CSS for maintainability: rules and guile lines to write CSS
As you start work on larger stylesheets and big projects with a team, you will discover that maintaining a huge CSS file can be challenging. So, we will go through some best practices for writing CSS that will help us to maintain the CSS project easily.
In this talk you will learn:
How to structure your JS-heavy project in Salesforce DX
How to structure your JS-heavy project in Salesforce DX
Learn how to use all the familiar JS tools with Webpack and Lightning
Totta ja tarua sähköautoista ja niiden lataamisestaVesa Linja-aho
Esitelmä Valkeakosken kirjastossa 27.4.2022. Asiaa sähköautoista, niiden lataamisesta ja paloturvallisuudesta. Ota yhteyttä jos joku kohta herättää kysymyksiä.
Hybridi-, sähkö- ja kaasuautojen turvallisuusperusteita pelastushenkilöstölleVesa Linja-aho
Sähkö ja kaasu kuljettaa – Hybridi-, sähkö- ja kaasuautojen turvallisuusperusteita pelastushenkilöstölle. Versio 1.2 (3.11.2014). ISBN 978-952-6690-27-8 (painettu), 978-952-6690-43-8 (pdf).
Kuinka bloggaaminen kehittää asiantuntijan ammattikuvaa ja henkilöbrändiä sek...Vesa Linja-aho
Kuinka bloggaaminen kehittää asiantuntijan ammattikuvaa ja henkilöbrändiä sekä auttaa lanseeraamaan uusia ajatuksia. Esitelmä Katleena Kortesuon Sano se someksi 1 -kirjan julkaisutilaisuudessa 4.4.2014.
How to use LaTeX and Beamer to prepare presentation for Slideshare
1. How to prepare Slideshare-compatible slides with
LaTeX and Beamer
Two simple tricks with the outlook and Scandinavian characters
Vesa Linja-aho
Metropolia
15. maaliskuuta 2011
Vesa Linja-aho How to prepare Slideshare-compatible slides with LaTeX and Bea
2. Tip 1: Eliminating the annoying clutter from the block
corners
If you use pdflatex to prepare your slides, they will look ok in
Adobe Reader, Mac Preview etc, but Slideshare embedded viewer
(as well as other Flash-based viewers I have seen) will add some
clutter to the box corners, like on this slide:
Vesa Linja-aho How to prepare Slideshare-compatible slides with LaTeX and Bea
3. Tip 1: Eliminating the annoying clutter from the block
corners
If you use pdflatex to prepare your slides, they will look ok in
Adobe Reader, Mac Preview etc, but Slideshare embedded viewer
(as well as other Flash-based viewers I have seen) will add some
clutter to the box corners.
Fix
For the final presentation to be uploaded, do not use pdflatex but
use: latex
dvips
ps2pdf
Vesa Linja-aho How to prepare Slideshare-compatible slides with LaTeX and Bea
4. Tip 2: Scandinavian characters (åäö)
Select encoding and enable local hyphenation rules
To be able to use Scandinavian characters, specify the encoding:
usepackage[utf8]{inputenc}
Selecting correct hyphenation rules for your language might be a
good idea:
usepackage[finnish]{babel}
Now the text will look great, but copy-paste from pdf will fail:
ääkköset will produce ¨aakk¨oset or something even more horrible.
Same applies to Slideshare Presentation Transcript!
Solution: load fonts with scandinavian symbols
Be sure to include both of these lines:
usepackage{ae,aecompl}
usepackage{lmodern}
Vesa Linja-aho How to prepare Slideshare-compatible slides with LaTeX and Bea