algorithm

On gradient-based methods for finding game-theoretic equilibria

On gradient-based methods for finding game-theoretic equilibria

February 4, 2020

Statistical decisions are often given meaning in the context of other decisions, particularly when there are scarce resources to be shared. Michael Jordan details the aim to blend gradient-based methodology with game-theoretic goals as part of a large "microeconomics meets machine learning" program.
Open-endedness: A new grand challenge for AI

Open-endedness: A new grand challenge for AI

February 4, 2020

We think a lot in machine learning about encouraging computers to solve problems, but there's another kind of learning, called open-endedness, that's just beginning to attract attention in the field. Kenneth Stanley walks you through how open-ended algorithms keep on inventing new and ever-more complex tasks and solving them continuallyeven endlessly.
Running large-scale machine learning experiments in the cloud

Running large-scale machine learning experiments in the cloud

February 3, 2020

Machine learning involves a lot of experimentation. Data scientists spend days, weeks, or months performing algorithm searches, model architecture searches, hyperparameter searches, etc. Shashank Prasanna breaks down how you can easily run large-scale machine learning experiments using containers, Kubernetes, Amazon ECS, and SageMaker.
The OS for AI: How serverless computing enables the next gen of machine learning

The OS for AI: How serverless computing enables the next gen of machine learning

February 2, 2020

ML has been advancing rapidly, but only a few contributors focus on the infrastructure and scaling challenges that come with it. Jonathan Peck explores why ML is a natural fit for serverless computing, a general architecture for scalable ML, and common issues when implementing on-demand scaling over GPU clusters, providing general solutions and a vision for the future of cloud-based ML.
Building machine learning inference pipelines at scale

Building machine learning inference pipelines at scale

January 31, 2020

Real-life ML workloads require more than training and predicting: data often needs to be preprocessed and postprocessed. Developers and data scientists have to train and deploy a sequence of algorithms that collaborate in delivering predictions from raw data. Julien Simon outlines how to build machine learning inference pipelines using open source libraries and how to scale them on AWS.
Digital discrimination: Cognitive bias in machine learning

Digital discrimination: Cognitive bias in machine learning

January 30, 2020

With increasing regularity we see stories in the news about machine learning algorithms causing real-world harm to people's lives and livelihoods. Maureen McElaney examines how bias can take root in machine learning algorithms and ways to overcome it.
Learn neural networks with Gonot math

Learn neural networks with Gonot math

January 28, 2020

Studying neural networks is a surefire way to end up fighting more math than you can shake a stick at. Wish you could learn about the likes of gradient descent and backpropagation in a language you actually understandlike Go? Then this one is for you. Join Ellen Korbes to learn neural networks with code, not math, and algorithms, not logarithms.
Open source streaming analytics with the Kafka, Flink, Cassandra (KFC) stack

Open source streaming analytics with the Kafka, Flink, Cassandra (KFC) stack

January 27, 2020

Streaming analytics is a popular subject in enterprise organizations because customers want real-time experiences, such as notifications and advice based on online behavior and other users actions. Bas Geerdink details an open source reference solution for streaming analytics that covers many use cases that follow a "pipes and filters" pattern, built with Scala, Flink, Kafka, and Cassandra.
Removing unfair bias in machine learning using open source (sponsored by IBM)

Removing unfair bias in machine learning using open source (sponsored by IBM)

January 25, 2020

ML models are increasingly used to make decisions that impact lives. Ana Echeverri and Trisha Mahoney walk you through how to use the open source Python package AI Fairness 360, developed by IBM researchers, a comprehensive open source toolkit empowering users with metrics to check for unwanted bias in datasets and machine learning models and state-of-the-art algorithms to mitigate such bias.
The OS for AI: How serverless computing enables the next gen of machine learning

The OS for AI: How serverless computing enables the next gen of machine learning

January 24, 2020

ML has been advancing rapidly, but only a few contributors focus on the infrastructure and scaling challenges that come with it. Jonathan Peck explores why ML is a natural fit for serverless computing, a general architecture for scalable ML, and common issues when implementing on-demand scaling over GPU clusters, providing general solutions and a vision for the future of cloud-based ML.
What's your machine learning score?

What's your machine learning score?

January 23, 2020

ML in production is different than ML in an R&D environment. Tania Allard dives deep into a number of techniques to test your ML quality and decay in your R&D and production environments appropriately. You'll see examples of issues commonly encountered in the ML area and how to test and monitor your data, model development, and infrastructure.
Channel into the universe of eventually perfect distributed systems

Channel into the universe of eventually perfect distributed systems

January 22, 2020

Lena Hall takes you on an adventure into the multifaceted universe of ever-changing distributed systems.
The First Stable libcamera Release: A Call for Public API Review

The First Stable libcamera Release: A Call for Public API Review

January 19, 2020

libcamera will soon turn one year old and keeps advancing in its purpose to provide a complete userspace camera stack for Linux-based systems.Since its conception and initial developments, libcamera h …
GNES: An Opensource Generic Neural Elastic Search Framework for Searching Everything

GNES: An Opensource Generic Neural Elastic Search Framework for Searching Everything

January 16, 2020

Tencent receives tons of text, images and videos everyday. Searching efficiently and effectively means everything to us, and understanding the content is the key to improve the search accuracy. Recent …
Deep learning for recommender systems

Deep learning for recommender systems

January 12, 2020

The success of deep learning has reached the realm of structured data in the past few years, where neural networks have been shown to improve the effectiveness and predictability of recommendation engines. Oliver Gindele offers a brief overview of such deep recommender systems and explains how they can be implemented in TensorFlow.
Deep learning with TensorFlow and Spark using GPUs and Docker containers

Deep learning with TensorFlow and Spark using GPUs and Docker containers

January 12, 2020

Organizations need to keep ahead of their competition by using the latest AI, ML, and DL technologies such as Spark, TensorFlow, and H2O. The challenge is in how to deploy these tools and keep them running in a consistent manner while maximizing the use of scarce hardware resources, such as GPUs. Thomas Phelan discusses the effective deployment of such applications in a container environment.
Evaluating cybersecurity defenses with a data science approach

Evaluating cybersecurity defenses with a data science approach

January 12, 2020

Cybersecurity analysts are under siege to keep pace with the ever-changing threat landscape. The analysts are overworked as they are bombarded with and burned out by the sheer number of alerts that they must carefully investigate. Brennan Lodge and Jay Kesavan explain how to use a data science model for alert evaluations to empower your cybersecurity analysts.
Learning "learning to rank"

Learning "learning to rank"

January 9, 2020

Identifying relevant documents quickly and efficiently enhances both user experience and business revenue every day. Sophie Watson demonstrates how to implement learning-to-rank algorithms and provides you with the information you need to implement your own successful ranking system.
On the accountability of black boxes: How we can control what we cant exactly measure

On the accountability of black boxes: How we can control what we cant exactly measure

January 7, 2020

Black box algorithmic systems make decisions that have a great impact in our lives. Thus, the need for their accountability and transparency is growing. Code4Thought created an evaluation model reflecting the state of practice in several organizations. Yiannis Kanellopoulos explores this model and shares lessons learned from its application at a financial corporation.
Practicing data science: A collection of case studies

Practicing data science: A collection of case studies

January 7, 2020

Rosaria Silipo shares a collection of past data science projects. While the structure is often similardata collection, data transformation, model training, deploymenteach required its own special trick, whether a change in perspective or a particular technique to deal with special case and special business questions.
Reading China: Predicting policy change with machine learning

Reading China: Predicting policy change with machine learning

January 7, 2020

Weifeng Zhong shares a machine learning algorithm built to read the Peoples Daily (the official newspaper of the Communist Party of China) and predict changes in Chinas policy priorities. The output of this algorithm, named the Policy Change Index (PCI) of China, turns out to be a leading indicator of the actual policy changes in China since 1951.
Reinforcement learning: A gentle introduction and an industrial application

Reinforcement learning: A gentle introduction and an industrial application

January 6, 2020

Reinforcement learning (RL) learns complex processes autonomously like walking, beating the world champion in Go, or flying a helicopter. No big datasets with the right answers are needed: the algorithms learn by experimenting. Christian Hidber shows how and why RL works and demonstrates how to apply it to an industrial hydraulics application with 7,000 clients in 42 countries.
Spark NLP in action: How Indeed applies NLP to standardize rsum content at scale

Spark NLP in action: How Indeed applies NLP to standardize rsum content at scale

January 6, 2020

Alexander Thomas and Alexis Yelton demonstrate how to use Spark NLP and Apache Spark to standardize semistructured text, illustrated by Indeed's standardization process for rsum content.
Best practices for scaling modeling platforms

Best practices for scaling modeling platforms

January 2, 2020

Companies are increasingly building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. Scott Clark and Matt Greenwood share a case study from a leading algorithmic trading firm to illustrate best practices for building these types of platforms in any industry.
Cognitive data science: The correct algorithm makes all the difference (sponsored by Deloitte Consulting)

Cognitive data science: The correct algorithm makes all the difference (sponsored by Deloitte Consulting)

January 1, 2020

Bill Roberts discusses artificial intelligence for strategic business insight and for the solution of new business problems using advanced cognitive algorithms. Along the way, he highlights the importance of using the right algorithm for a given business challenge, using real-world examples.
Deep learning for recommender systems, Or How to compare pears with apples

Deep learning for recommender systems, Or How to compare pears with apples

January 1, 2020

Recommender systems support decision making with personalized suggestions and have proven useful in ecommerce, entertainment, and social networks. Sparse data and linear models are a burden, but the application of deep learning sets new boundaries and offers remarkable results. Join Marcel Kurovski to explore a use case for vehicle recommendations at Germany's biggest online vehicle market.
Executive Briefing: 5 key questions to kick off your AI implementation

Executive Briefing: 5 key questions to kick off your AI implementation

January 1, 2020

The significant hype bubble building up around AI has convinced many executives that if theyre not already tech savvy, they might not be ready for AIs transformative power. However, the reality is that AI is just another tool that can help your business, and youre probably not that far behind. Vinay Seth Mohta explains how to evaluate AI as you would any other strategic investment.
Executive Briefing: Fear and loathing in explainability and transparencyA savage journey to the heart of AI

Executive Briefing: Fear and loathing in explainability and transparencyA savage journey to the heart of AI

January 1, 2020

Jana Eggers explores explainability and transparency as both required and unachievable goals for AI, with a focus on helping teams structure discussions about levels of explainability possible and needed for both user trust and regulatory requirements.