# Tag: Optimization (36)

**TensorFlow: What Parameters to Optimize?**- Nov 9, 2017.

Learning TensorFlow Core API, which is the lowest level API in TensorFlow, is a very good step for starting learning TensorFlow because it let you understand the kernel of the library. Here is a very simple example of TensorFlow Core API in which we create and train a linear regression model.**AI Conference in San Francisco, Sep 2017 – highlights and key ideas**- Sep 28, 2017.

Highlights from recent AI Conference include the inevitable merger of IQ and EQ in computing, Deep learning to fight cancer, AI as the new electricity and advice from Andrew Ng, Deep reinforcement learning advances and frontiers, and Tim O’Reilly analysis of concerns that AI is the single biggest threat to the survival of humanity.**Closing the Insights-to-Action Gap**- Sep 5, 2017.

There are many types of analytics for getting insight out of data, but the bigger and more difficult challenge is turning that insight into action. What should we do differently based on your findings?**What Is Optimization And How Does It Benefit Business?**- Aug 10, 2017.

Here we explain what Mathematical Optimisation is, and discuss how it can be applied in business and finance to make decisions.**When not to use deep learning**- Jul 24, 2017.

Despite DL many successes, there are at least 4 situations where it is more of a hindrance, including low-budget problems, or when explaining models and features to general public is required.

**Optimizing Web sites: Advances thanks to Machine Learning**- Jul 17, 2017.

Machine learning has revitalized a nearly dormant method, leading to a powerful approach for optimizing Web pages, finding the best of thousands of alternatives.**Optimization in Machine Learning: Robust or global minimum?**- Jun 30, 2017.

Here we discuss how convex problems are solved and optimised in machine learning/deep learning.**Introducing Dask-SearchCV: Distributed hyperparameter optimization with Scikit-Learn**- May 12, 2017.

We introduce a new library for doing distributed hyperparameter optimization with Scikit-Learn estimators. We compare it to the existing Scikit-Learn implementations, and discuss when it may be useful compared to other approaches.**Learning to Learn by Gradient Descent by Gradient Descent**- Feb 2, 2017.

What if instead of hand designing an optimising algorithm (function) we*learn*it instead? That way, by training on the class of problems we’re interested in solving, we can learn an optimum optimiser for the class!**5 Machine Learning Projects You Can No Longer Overlook, January**- Jan 2, 2017.

There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects, the most recent in an ongoing series.

**Game Theory Reveals the Future of Deep Learning**- Dec 29, 2016.

This post covers the emergence of Game Theoretic concepts in the design of newer deep learning architectures. Deep learning systems need to be adaptive to imperfect knowledge and coordinating systems, 2 areas with which game theory can help.**The hard thing about deep learning**- Dec 1, 2016.

It’s easy to optimize simple neural networks, let’s say single layer perceptron. But, as network becomes deeper, the optmization problem becomes crucial. This article discusses about such optimization problems with deep neural networks.**How to Make Your Database 200x Faster Without Having to Pay More**- Nov 22, 2016.

Waiting long for a BI query to execute? I know it’s annoyingly frustrating… It’s a major bottle neck in day-to-day life of a Data Analyst or BI expert. Let’s learn some of the easy to use solutions and a very good explanation of why to use them, along with other advanced technological solutions.**The Hard Problems AI Can’t (Yet) Touch**- Jul 11, 2016.

It's tempting to consider the progress of AI as though it were a single monolithic entity, advancing towards human intelligence on all fronts. But today's machine learning only addresses problems with simple, easily quantified objectives**Three Impactful Machine Learning Topics at ICML 2016**- Jul 1, 2016.

This post discusses 3 particular tutorial sessions of impact from the recent ICML 2016 conference held in New York. Check out some innovative ideas on Deep Residual Networks, Memory Networks for Language Understanding, and Non-Convex Optimization.**Angoss 9.6 Data Science Software Suite**- Apr 29, 2016.

Angoss software provides users with comprehensive scorecard building functionality that is fast, reliable, accurate, and business centric.**Metrics Gone Wrong – How Companies Are Optimizing The Wrong Way**- Apr 20, 2016.

A critique of the over-abundant and misguided pursuit of metric completeness, and how it can result in incorrect "optimization."**Stanford: Data Mining, Data Science Online Courses, Certificate**- Feb 22, 2016.

Take online Data Mining and Data Science courses with top Stanford faculty that count toward a Stanford Graduate Certificate. Spring quarter starts in March - enroll now.**Stanford Big Data Mining Certificates Online**- Nov 10, 2015.

Earn online certificates in Mining Massive Data Sets, Data Mining, Optimization, and more while learning from world-renowned Stanford experts.**YCML Machine Learning library on Github**- Aug 24, 2015.

YCML is a new Machine Learning library available on Github as an Open Source (GPLv3) project. It can be used in iOS and OS X applications, and includes Machine Learning and optimization algorithms.**HP Big Data Helps Ford to Better Manage Fleets and Personalize Employee Drives**- Jul 23, 2015.

HP-Ford partnership is leveraging Big Data for the next level of Telematics insights based intelligence.**Interview: Ksenija Draskovic, Verizon on Dissecting the Anatomy of Predictive Analytics Projects**- Apr 15, 2015.

We discuss Predictive Analytics use cases at Verizon Wireless, advantages of a unified data view, model selection and common causes of failure.**Interview: Lei Shi, ChinaHR.com on Analytics behind the Perfect Match**- Mar 6, 2015.

We discuss analytics at ChinaHR, matching job seekers and employers, traditional job fairs vs online recruitment, key metrics and analytical insights.**Interview: Jason Bloomberg, Intellyx on the Tricky Balance of Optimization and Innovation**- Feb 6, 2015.

We discuss Agile Digital Transformation, Optimization vs Innovation trade-off, best innovations of 2014, trends, advice and more.**FICO Analytics Competition: Helping Santa Helpers**- Dec 11, 2014.

FICO is sponsoring a Kaggle competition to optimize Santa's scheduling algorithm. Competitors can win in multiple categories and the best solution using FICO Xpress Optimization Suite will receive a special prize.**LION Intelligent Learning and Optimization News**- Nov 26, 2014.

LION intelligent learning and optimization adds full support for Java packages, new visualization neatly explains overfitting, and get "The LION way" book on Kindle (free if you qualify).**LIONoso, a non-profit tool for Machine Learning and Intelligent Optimization**- Nov 3, 2014.

LIONoso 2.1, developed by LIONlab for non-profit research and academic use, offers flexible training and experimentation for researchers who know what they are doing and want freedom in their ML experimentation.**Book: Modern Optimization with R**- Oct 10, 2014.

Learn the most relevant concepts related to modern optimization methods and how to apply them using multi-platform, open source, R tools in this new book on metaheuristics.**Interview: Pallas Horwitz, Blue Shell Games on Why Data Science is So Critical for Gaming Studios**- Aug 14, 2014.

We discuss the role of data science at Blue Shell Games, the importance of "Lean Data", key metrics for online games, cross-product projects and optimizing meeting the data needs across an organization.**XLMiner solves Big Data Problems in Excel**- Jun 26, 2014.

XLMiner, a part of Analytic Solver Platform integrated software for predictive and prescriptive analytics - forecasting, data mining, optimization and simulation, lets you solve small or Big Data problems in Excel.**LION Resources for Teaching Machine Learning and Optimization**- Jun 23, 2014.

A great collection of resources for "LION: Learning and Intelligent Optimization" textbook includes slides, tutorial movies, exercises, use cases, and LIONoso - an academic version of LIONsolver software.**The Algorithm that Runs the World Can Now Run More of It**- Jun 13, 2014.

The most important algorithm, used for optimizing almost everything, is linear programming. New advances allow linear programming problems to be solved faster using the new commercial parallel simplex solver.**MADALGO Summer School on LEARNING AT SCALE, August 11-14, Denmark**- May 22, 2014.

MADALGO Summer School will teach the latest developments in learning at scale as applied to Big Data. Registration is free on a first-come-first serve basis. Denmark, Aug 11 - 14, 2014.**WCAI: Measuring Skill Level and Optimizing Player-Matching Algorithms in Online Games**- Mar 26, 2014.

New research opportunity from Wharton Customer Analytics Initiative (WCAI) involves a unique data set from a major gaming company, with historic behavioral data for 9.5 million users who played 882K games. Learn more on Apr 25.**Top stories for Mar 9-15: How Many Data Scientists?**- Mar 16, 2014.

How Many Data Scientists are out there? LIONbook: Machine Learning + Intelligent Optimization - completed, free personal download; Boston AnalyticsWeek: Big Data and Analytics Unconference, March 24-28; Upcoming Webcasts on Analytics, Big Data, Data Science.**LIONbook: Machine Learning + Intelligent Optimization – completed, free personal download**- Mar 11, 2014.

This book combines two usually separated topics: machine learning and intelligent optimization, and does it with enough technical details to satisfy professionals, but also with concrete examples, vivid images, and fun. Buy a low-cost paperback or ebook (Kindle), or download a free PDF.