Analytics Vidhya

MARCH 13, 2019

Introduction I have been a programmer since before I can remember. I enjoy writing codes from scratch – this helps me understand that topic. The post 5 Amazing Deep Learning Frameworks Every Data Scientist Must Know! (with Illustrated Infographic) appeared first on Analytics Vidhya.

Deep Learning 308

Deep Learning Deep Learning Data Scientist Analytics 308

Dataconomy

MARCH 14, 2019

Here is how Big Data can help you in your growth strategies in ecommerce Ecommerce is taking a bigger slice of the global retail pie. In the US, for example, ecommerce currently accounts for approximately 10% of all retail sales, a number that’s projected to swell to nearly 18% by. The post Data’s Growing Role in Scalable Ecommerce? appeared first on Dataconomy.

Big Data 195

Big Data Big Data Analytics Analytics 195

Data Science 101

MARCH 9, 2019

Machine Learning for Kids is a site for children and teachers to explore machine learning with the Scratch Programming language. It includes numerous lessons and tutorials for building fun programs which incorporate machine learning.

Machine Learning 81

Machine Learning Machine Learning Data Science AI 81

DataRobot

MARCH 14, 2019

For the Philadelphia 76ers, data is an integral part of how they work, helping them make strategic decisions on both the sports and corporate sides of the organization. On the business side, they knew that taking a data-driven approach could help them become much more efficient in how they approach their ticket sales process. Enter automated machine learning with the layup.

Analytics 78

Analytics Analytics Machine Learning Machine Learning 78

Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage

When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m

Analytics Vidhya

MARCH 10, 2019

Introduction I work on different Natural Language Processing (NLP) problems (the perks of being a data scientist!). Each NLP problem is a unique challenge in. The post A Step-by-Step NLP Guide to Learn ELMo for Extracting Features from Text appeared first on Analytics Vidhya.

Natural Language Processing 301

Natural Language Processing Data Scientist Analytics Analytics 301

Dataconomy

MARCH 15, 2019

Moving IT to the cloud is one of the main objectives for many companies. But once achieved, they find managing data in a hybrid environment a challenge. This is when metadata management becomes more important than ever. Cloud vs. on-premises? It’s one of those topics that you hear broached at. The post How Agile Cloud Solutions Can Upgrade On-Prem Performance: The BI Business Case?

Cloud Computing 157

Cloud Computing Business Intelligence Business Intelligence Analytics 157

Victor Zhou

MARCH 14, 2019

It all started when I was 12 years old. ? Back in those days, my friends and I all played Runescape , a popular browser-based MMORPG. Those who’ve played Runescape will remember that it requires lots of grinding - the “best” players were the ones who spent the most time killing monsters and leveling up. Unfortunately, my parents didn’t let me play much - all of my friends had passed level 50 by the time I reached level 30.

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Machine Learning World

MARCH 13, 2019

In this article i want to summarize all things that u will need to make good dev environment and deployment for a small application. To make this happen we will use AWS Free Tie r and Docker containers and orchestration and Django app as a typical project Link on this project github: [link] Before go farther please install Docker first: [link] All code running under Python 3.6 Docker Docker is a container virtualization engine that gives u ability to create cheap and fast environments for produc

AWS 52

AWS Python Azure Machine Learning 52

DataRobot

MARCH 12, 2019

To fully exploit AI’s potential across the enterprise, organizations should democratize DataRobot automated machine learning models in their existing analytics tools, processes, and apps. By bringing AI to your users, you can improve bottom line outcomes. In this series, I will show you a wide variety of options to infuse DataRobot machine learning models into your favorite analytics tools.

Analytics 58

Analytics Analytics Machine Learning Machine Learning 58