# Tag: Mathematics (47)

**Math for Programmers.**- Jun 7, 2019.

Math for Programmers teaches you the math you need to know for a career in programming, concentrating on what you need to know as a developer.**Math for Machine Learning.**- Jun 5, 2019.

This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.**KDnuggets™ News 19:n20, May 22: 7 Steps to Mastering SQL for Data Science; How to build Math Programming Skills**- May 22, 2019.

Also An overview of Pycharm for Data Scientists; How to build a Computer Vision model - key approaches and datasets; k-means clustering tutorial; 60+ useful graph visualization libraries; The Data Fabric for Machine Learning.**Probability Mass and Density Functions**- May 21, 2019.

This content is part of a series about the chapter 3 on probability from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville, A. (2016). It aims to provide intuitions/drawings/python code on mathematical theories and is constructed as my understanding of these concepts.**Mathematical programming — Key Habit to Build Up for Advancing Data Science**- May 15, 2019.

We show how, by simulating the random throw of a dart, you can compute the value of pi approximately. This is a small step towards building the habit of mathematical programming, which should be a key skill in the repertoire of a budding data scientist.**Math for Programmers**- Apr 11, 2019.

Math for Programmers teaches you the math you need to know for a career in programming, concentrating on what you need to know as a developer.**Monetizing the Math – are you ready?**- Jan 28, 2019.

We outline an extensive list of things to do or plan for to help fully realize the ROI of your AI and Machine Learning projects in 2019.**Math for Programmers**- Jan 15, 2019.

Math for Programmers teaches you the math you need to know for a career in programming, concentrating on what you need to know as a developer.**Math for Machine Learning**- Jan 4, 2019.

This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.**Southern Illinois University Edwardsville: Director of the Center for Predictive Analytics/(Associate) Professor of Mathematics and Statistics [Edwardsville, IL]**- Jan 4, 2019.

Southern Illinois University Edwardsville (SIUE) is establishing the Center for Predictive Analytics (C-PAN), and is seeking an innovative, visionary director for the center who will provide centralized leadership in establishing research and educational initiatives across academic units at SIUE.**Math for Machine Learning**- Dec 10, 2018.

This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.**A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more**- Dec 7, 2018.

A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.**New Book: Linear Algebra – what you need for Machine Learning and Data Science now**- Oct 24, 2018.

From machine learning and data science to engineering and finance, linear algebra is an important prerequisite for the careers of today and of the future. Learn the math you need with this book.**Top KDnuggets tweets, Oct 10-16: 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning; 6 Books Every Data Scientist Should Keep Nearby**- Oct 17, 2018.

Also Machine Learning Cheat Sheets; Top 8 Free Must-Read Books on Deep Learning.**University of San Francisco: Assistant Professor, Tenure Track, Mathematics and Statistics [San Francisco, CA]**- Oct 17, 2018.

The University of San Francisco invites applications for a tenure-track Assistant Professor position to begin August 2019. We seek well-qualified candidates in the areas of applied mathematics or statistics, with a focus on the extraction of knowledge from data.**Preprocessing for Deep Learning: From covariance matrix to image whitening**- Oct 10, 2018.

The goal of this post/notebook is to go from the basics of data preprocessing to modern techniques used in deep learning. My point is that we can use code (Python/Numpy etc.) to better understand abstract mathematical notions!**KDnuggets™ News 18:n37, Oct 3: Mathematics of Machine Learning; Effective Transfer Learning for NLP; Path Analysis with R**- Oct 3, 2018.

Also: Introducing VisualData: A Search Engine for Computer Vision Datasets; Raspberry Pi IoT Projects for Fun and Profit; Recent Advances for a Better Understanding of Deep Learning; Basic Image Data Analysis Using Python - Part 3; Introduction to Deep Learning**Math for Machine Learning**- Sep 28, 2018.

**Learning mathematics of Machine Learning: bridging the gap**- Sep 28, 2018.

We outline the four key areas of Maths in Machine Learning and begin to answer the question: how can we start with high school maths and use that knowledge to bridge the gap with maths for AI and Machine Learning?**KDnuggets™ News 18:n34, Sep 12: Essential Math for Data Science; 100 Days of Machine Learning Code; Drop Dropout**- Sep 12, 2018.

Also: Neural Networks and Deep Learning: A Textbook; Don't Use Dropout in Convolutional Networks; Ultimate Guide to Getting Started with TensorFlow.**Machine Learning Cheat Sheets**- Sep 11, 2018.

Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.**Essential Math for Data Science: ‘Why’ and ‘How’**- Sep 6, 2018.

It always pays to know the machinery under the hood (even at a high level) than being just the guy behind the wheel with no knowledge about the car.**Unveiling Mathematics Behind XGBoost**- Aug 14, 2018.

Follow me till the end, and I assure you will atleast get a sense of what is happening underneath the revolutionary machine learning model.**Math for Machine Learning: Open Doors to Data Science and Artificial Intelligence**- Jul 18, 2018.

**Learn AI and Data Science rapidly based only on high school math – KDnuggets Offer**- May 25, 2018.

This 3-month program, created by Ajit Jaokar, who teaches at Oxford, is interactive and delivered by video. Coding examples are in Python. Places limited - check special KDnuggets rate.**Top KDnuggets tweets, May 02-08: Boost your data science skills. Learn linear algebra.**- May 9, 2018.

Also: #ApacheSpark: #Python vs. #Scala pros and cons for #DataScience; Loc2Vec: Learning location embeddings with triplet-loss networks; Skewness vs Kurtosis - The Robust Duo.**24houranswers: Analytics / Data Science / Math / Statistics Tutors**- May 9, 2018.

Seeking qualified Ph.D. students or faculty members for the position of Tutor/Instructor to provide one-on-one lectures to the needs of our students in Applied Analytics, Computer Science, Applied Math and Statistics, and more.**KDnuggets™ News 18:n19, May 9: KDnuggets Poll: What tools you used for Analytics/Data Science Projects? 8 Useful Advices for Aspiring Data Scientists**- May 9, 2018.

Also: Boost your data science skills. Learn linear algebra; Apache Spark: Python vs. Scala; Getting Started with spaCy for Natural Language Processing.**Deep Conversations: Lisha Li, Principal at Amplify Partners**- May 3, 2018.

Mathematician Lisha Li expounds on how she thrives as a Venture Capitalist at Amplify Partners to identify, invest and nurture the right startups in Machine Learning and Distributed Systems.**Boost your data science skills. Learn linear algebra.**- May 3, 2018.

The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms.**Top KDnuggets tweets, Apr 11-17: Boost your #datascience skills. Learn linear algebra.**- Apr 18, 2018.

Also: Don’t learn #MachineLearning in 24 hours; Top 8 Free Must-Read Books on #DeepLearning; How Attractive Are You in the Eyes of Deep #NeuralNetwork?; Ten #MachineLearning Algorithms You Should Know to Become a #DataScientist**7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning**- Apr 17, 2018.

It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.**R Fundamentals: Building a Simple Grade Calculator**- Mar 19, 2018.

In this tutorial, we'll teach you the basics of R by building a simple grade calculator. While we do not assume any R-specific knowledge, you should be familiar with general programming concepts.**Data Structures Related to Machine Learning Algorithms**- Jan 30, 2018.

If you want to solve some real-world problems and design a cool product or algorithm, then having machine learning skills is not enough. You would need good working knowledge of data structures.**How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?**- Dec 20, 2017.

When I started diving deep into these exciting subjects (by self-study), I discovered quickly that I don’t know/only have a rudimentary idea about/ forgot mostly what I studied in my undergraduate study some essential mathematics.**Top KDnuggets tweets, Aug 24-30: #DataScientist – sexiest job of the 21st century until …; Activation Function in #NeuralNetworks.**- Aug 31, 2016.

Cartoon: #DataScientist - sexiest job of the 21st century until ...; What is the Role of the Activation Function in Neural Networks?; LinkedIn Machine Learning team tutorial on building #Recommender system; Create a #Chatbot for #Telegram in #Python to Summarize Text.**Open Source Machine Learning Degree**- Jun 6, 2016.

A set of free resources for learning machine learning, inspired by similar open source degree resources. Find links to books and book-length lecture notes for study.**Practical skills that practical data scientists need**- May 13, 2016.

The long story short, data scientist needs to be capable of solving business analytics problems. Learn more about the skill-set you need to master to achieve so.**Data scientists mostly just do arithmetic and that’s a good thing**- May 10, 2016.

Are you also wondering how you can get started as data scientist, and become a valuable team player. Understand what really matters as data scientist, and things to focus in the initial stages.**There is more to a successful data scientist than mere knowledge**- May 9, 2016.

Look at Data scientist "definitions" with a wry smile: the "essential" skills very much reflect those that a short time ago were quite novel, and are being used in applications to problems that have recently become solvable.**The Evolution of the Data Scientist**- Mar 16, 2016.

We trace the evolution of Data Science from ancient mathematics to statistics and early neural networks, to present successes like AlphaGo and self-driving car, and look into the future.**How to Tackle a Lottery with Mathematics**- Jan 27, 2016.

With mathematical rigor and narrative flair, Adam Kucharski reveals the tangled history of betting and science. The house can seem unbeatable. In this book, Kucharski shows us just why it isn't. Even better, he shows us how the search for the perfect bet has been crucial for the scientific pursuit of a better world.**15 Mathematics MOOCs for Data Science**- Sep 23, 2015.

The essential mathematics necessary for Data Science can be acquired with these 15 MOOCs, with a strong emphasis on applied algebra & statistics.**Deep Learning and the Triumph of Empiricism**- Jul 7, 2015.

Theoretical guarantees are clearly desirable. And yet many of today's best-performing supervised learning algorithms offer none. What explains the gap between theoretical soundness and empirical success?**Top KDnuggets tweets, Oct 31 – Nov 2: Nate Silver on 3 Keys to Great Information Design**- Nov 3, 2014.

Best Infographics of the Year: Nate Silver on 3 Keys to Great Information Design; The invention of the Equals Sign jump-started math; A Great Collection of Machine Learning Algorithms; LinkedIn breaking up its data science team.**Top KDnuggets tweets, Feb 26-27: Facebook “Relationship Data Mining”; Non-human math: Wikipedia-size computer proof**- Feb 28, 2014.

The gap between data mining and predictive models in Facebook Relationship Data Mining; Non-human math: Computer proof is Wikipedia size; A look at pioneering women in #BigData ; MS in Data Analytics from CUNY: Online and Affordable.**Analytics, Mathematics, and Art of Rene Romero Schuler and Van Gogh**- Jan 13, 2014.

Is there a connection between Analytics, Mathematics and arts? How do we add arts flavor to analytics business is the question. And answer lies in century old painting by Van Gogh called "The Starry Night" which represents perfect turbulence.