Software Engineer in Machine Learning/siliconarmada.com

#artificialintelligence

Your mission We are searching for great machine learning engineers to join the team responsible for: · Extending Criteo's large scale distributed machine learning library (e.g., implementing new distributed and scalable machine learning algorithms, improving their performance) · Building and improving prediction models for ad targeting; proving the business value of the changes and deploying them to production · Gathering and analyzing data, performing statistical modeling You'll have the opportunity to work on highly challenging problems with both engineering and scientific aspects; for example: · Click prediction:ÂHow do you accurately predict in less than a millisecond if the user will click on an ad? Thankfully, you have billions of datapoints to help you. · Offline testing:ÂYou can always compute the classification error on a model predicting the click probability. But will it really correlate with the online performance of this model? · Explore / exploit:ÂIt's easy, UCB and Thomson sampling have low regret. But what happens when new products come and go and when each ad displayed changes the reward of each arm? But what do you do when all data are not equal and when you must distribute the learning overÂthousandsÂof nodes? To qualify for this mission, you need: · MS degree in Computer Science or related quantitative field with 3 years of relevant experience or Ph.D degree in Computer Science or related quantitative field · Good understanding of the mathematical foundations behind machine learning algorithms · Great coding skills.


Big Data Science: Expectations vs. Reality

@machinelearnbot

The past few years has been like a dream come true for those who work in analytics and big data. There is a new career path for platform engineers to learn Hadoop, Scala and Spark. Java and Python programmers have a chance to move to the Big Data world. There they find higher salaries, new challenges and get to scale up to distributed systems. But recently I am starting to hear some complaints and dashed hopes from engineers who have spent time working there.


Big Data Science: Expectations vs. Reality

@machinelearnbot

The past few years has been like a dream come true for those who work in analytics and big data. There is a new career path for platform engineers to learn Hadoop, Scala and Spark. Java and Python programmers have a chance to move to the Big Data world. There they find higher salaries, new challenges and get to scale up to distributed systems. But recently I am starting to hear some complaints and dashed hopes from engineers who have spent time working there.


Pro Hadoop Data Analytics: Designing and Building Big Data Systems using the Hadoop Ecosystem: Kerry Koitzsch: 9781484219096: Amazon.com: Books

@machinelearnbot

Kerry Koitzsch is a software engineer and interested in the early history of science, particularly chemistry. He frequently publishes papers and attends conferences on scientific and historical topics, including early chemistry and alchemy, and sociology of science. He has presented many lectures, talks, and demonstrations on a variety of subjects for the United States Army, the Society for Utopian Studies, American Association for Artificial Intelligence (AAAI), Association for Studies in Esotericism (ASE), and others. He has also published several papers and written two historical books. Kerry was educated at Interlochen Arts Academy, MIT, and the San Francisco Conservatory of Music.


Pro Apache Hadoop: 9781430248637: Computer Science Books @ Amazon.com

@machinelearnbot

This book is excellent, and this is coming from a guy who hated almost every other hadoop book he read. There are a couple things that these authors do different from other hadoop authors: 1. They thoroughly explain the difference between all the hadoop versions, and what things are compatible/incompatible with one another. It covers YARN in detail. This is so important because a lot of code has already been developed for the old API, and people are likely to encounter that at work. 3.