math

Zero to ML hero with TensorFlow 2.0

Zero to ML hero with TensorFlow 2.0

February 22, 2020

Get a programmer's perspective on machine learning with Laurence Moroney, from the basics all the way up to building complex computer vision scenarios using convolutional neural networks and natural language processing with recurrent neural networks.
Deploying machine learning models on the edge

Deploying machine learning models on the edge

February 20, 2020

When IoT meets AI, a new round of innovations begins. Yan Zhang and Mathew Salvaris examine the methodology, practice, and tools around deploying machine learning models on the edge. They offer a step-by-step guide to creating an ML model using Python, packaging it in a Docker container, and deploying it as a local service on an edge device as well as deployment on GPU-enabled edge devices.
Executive Briefing: Fusing data and design

Executive Briefing: Fusing data and design

February 19, 2020

Data scientists feel naturally comfortable with the language of mathematics, while designers think in the language of human empathy. Creating a bridge between the two was essential to the success of a recent project at an energy company. Tim Daines and Philip Pilgerstorfer detail what they learned while creating these bridges, showcasing techniques through a series of aha moments.
ROCm and Hopsworks for end-to-end deep learning pipelines

ROCm and Hopsworks for end-to-end deep learning pipelines

February 18, 2020

The Radeon open ecosystem (ROCm) is an open source software foundation for GPU computing on Linux. ROCm supports TensorFlow and PyTorch using MIOpen, a library of highly optimized GPU routines for deep learning. Jim Dowling and Ajit Mathews outline how the open source Hopsworks framework enables the construction of horizontally scalable end-to-end machine learning pipelines on ROCm-enabled GPUs.
Deep learning methods for natural language processing

Deep learning methods for natural language processing

February 15, 2020

Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include Word2Vec, recurrent neural networks (RNNs) and variants (long short-term memory [LSTM] and gated recurrent unit [GRU]), and convolutional neural networks.
Democratization of data science: Using machine learning to build credit risk models

Democratization of data science: Using machine learning to build credit risk models

February 14, 2020

Tokyo Century was ready for a change. Credit risk decisions were taking too long and the home office was taking notice. The company needed a full stack data solution to increase the speed of loan authorizations, and it needed it quickly. Moto Tohda explains how Tokyo Century put data at the center of its credit risk decision making and removed institutional knowledge from the process.
The Unified Tracing Platform

The Unified Tracing Platform

February 3, 2020

One of the complaints about Linux is that there are more than one way to skin the tracing cat. There's ftrace, perf, BPF, bpftrace, LTTng, strace, gdb, and all sorts of ways to accomplish tracing with …
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.
Learning the Linux Kernel Configuration Space: Results and Challenges

Learning the Linux Kernel Configuration Space: Results and Challenges

January 10, 2020

Given a configuration, can humans know in advance the size, the compilation time, or the boot time of a Linux kernel?Owing to the huge complexity of Linux (there are more than 15000 options with hard …
Insightful health: Amplifying intelligence in healthcare patient flow execution

Insightful health: Amplifying intelligence in healthcare patient flow execution

January 9, 2020

Fabio Ferraretto and Claudia Regina Laselva explain how Hospital Albert Einstein and Accenture evolved patient flow experience and efficiency with the use of applied AI, statistics, and combinatorial math, allowing the hospital to anticipate E2E visibility within patient flow operations, from admission of emergency and elective demands to assignment and medical releases.
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.
Better Together Diversity Networking Lunch

Better Together Diversity Networking Lunch

January 2, 2020

If youd like to make new professional connections and hear ideas for supporting diversity in the tech community, come to the diversity and inclusion networking lunch on Wednesday.
Beyond Word2Vec: Using embeddings to chart out the ebb and flow of tech skills

Beyond Word2Vec: Using embeddings to chart out the ebb and flow of tech skills

January 2, 2020

Word embeddings such as word2vec have revolutionized language modeling. Maryam Jahanshahi discusses exponential family embeddings, which apply probabilistic embedding models to other data types. Join in to learn how TapRecruit implemented a dynamic embedding model to understand how tech skill sets have changed over three years.
Deep learning methods for natural language processing

Deep learning methods for natural language processing

January 1, 2020

Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include word2vec, recurrent neural networks and variants (LSTM, GRU), and convolutional neural networks.
Deploying deep learning models on GPU-enabled Kubernetes clusters

Deploying deep learning models on GPU-enabled Kubernetes clusters

January 1, 2020

Interested in deep learning models and how to deploy them on Kubernetes at production scale? Not sure if you need to use GPUs or CPUs? Mathew Salvaris and Fidan Boylu Uz help you out by providing a step-by-step guide to creating a pretrained deep learning model, packaging it in a Docker container, and deploying as a web service on a Kubernetes cluster.
Applications of mixed effects random forests

Applications of mixed effects random forests

December 27, 2019

Clustered data is all around us. The best way to attack it? Mixed effect models. Sourav Dey explains how the mixed effects random forests (MERF) model and Python package marries the world of classical mixed effect modeling with modern machine learning algorithms and shows how it can be extended to be used with other advanced modeling techniques like gradient boosting machines and deep learning.
Applied machine learning in finance

Applied machine learning in finance

December 27, 2019

Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both sell-side and buy-side institutions. Chakri Cherukuri explains how machine learning and deep learning techniques are being used in quantitative finance and details how these models work under the hood.
Managing data science in the enterprise

Managing data science in the enterprise

December 22, 2019

The honeymoon era of data science is ending, and accountability is coming. Successful data science leaders must deliver measurable impact on an increasing share of an enterprise's KPIs. Joshua Poduska, Kimberly Shenk, and Mac Steele explain how leading organizations take a holistic approach to people, process, and technology to build a sustainable competitive advantage.
Online evaluation of machine learning models

Online evaluation of machine learning models

December 22, 2019

Evaluating machine learning models is surprisingly hard, particularly because these systems interact in very subtle ways. Ted Dunning breaks the problem of evaluation apart into operational and function evaluation, demonstrating how to do each without unnecessary pain and suffering. Along the way, he shares exciting visualization techniques that will help make differences strikingly apparent.