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Automating Data Wrangling – The Next Machine Learning Frontier
Up to 95% of a data scientist’s time is spent data wrangling. Conversely, about 99% of data-scientists hate data wrangling. That’s problematic. Data wrangling tends to be the most redundant and mind-numbing process associated with building Machine Learning (ML) models. There are four steps to building an ML model:... Read more
Ben Vigoda on the New Era of NLP
Many apps and programs claim to be able to understand you and are at least capable of engaging in superficial interactions. Spend long enough talking to one of these programs, however, and you’ll no doubt see the hallmarks of imperfectly reproduced natural language. That technology has not yet caught... Read more
Watch: Introduction to Machine Learning with Scikit-Learn
Machine learning has become an indispensable tool across many areas of research and commercial applications. From text-to-speech for your phone to detecting the Higgs-Boson particle, machine learning excels at extracting knowledge from large amounts of data. This talk gives a general introduction to machine learning and introduces practical tools... Read more
Watch: Deploying Investments in AI and Machine Learning
Over the next 18 months, companies will be completing the R&D phase of their AI/ML investments and will be deploying their models and algorithms to production. The proper execution of deploying your AI/ML models will separate the organizations who see an ROI on AI from those who don’t. This... Read more
Watch: High-Performance Data Science with Docker and Digital Ocean
This talk discusses Docker as a tool for data scientists, in particular in conjunction with the popular interactive programming platform, Jupyter, and the cloud computing platform, Amazon Web Services (AWS). Using Docker, Jupyter, and AWS, a data scientist can take control of their environment configuration, prototype scalable data architectures,... Read more
Watch: Applications of Deep Learning in Aerospace
Recent advances in machine learning techniques such as deep learning (DL) have rejuvenated data-driven analysis in aerospace and integrated building systems. DL algorithms have been successful due to the presence of large volumes of data and its ability to learn the features during the learning process. The performance improvement... Read more
Come See Our Talk on MATLAB and TensorFlow: 3 Ways to Enhance TensorFlow with MATLAB
Shounak Mitra, MathWorks’ Product Manager for Deep Learning Toolbox, will be presenting “everything but the training” at ODSC on Thursday, May 2nd at 2 PM in Room 202. Here are some of the highlights of the talk and why you should attend. In AI and deep learning workflows, a... Read more
Properly Setting the Random Seed in ML Experiments. Not as Simple as You Might Imagine
Join Comet at Booth 406 in the ODSC East Expo Hall. We will also be speaking at ODSC: – April 30, 9 am — A Deeper Stack for Deep Learning: Adding Visualizations + Data Abstractions to your Workflow (Douglas Blank, Head of Research) – May 2, 2:15 pm... Read more
Learn How to Organize, Cleanup and Process Medical Image Datasets for Computer Vision Training
We are entering a whole new world where the possibility for AI, and more specifically computer vision, can help us with medical decision making that we’ve relied on doctors to perform. Moreover, while the hope of having doctor-less diagnoses is still a work of science fiction, every day we... Read more
ODSC East DeepOps: Building an AI First Company
I’ve spoken to over a hundred AI companies as part of my job at MissingLink.ai and the result of analyzing their experiment, data and compute workflows. Certain challenges were a common theme across many teams – the solutions to these is a concept we call “DeepOps”, deep learning operations.... Read more