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How to Choose Machine Learning or Deep Learning for Your Business
AI is the future, or so you’re hearing. Every day, news of another organization leveraging AI to produce business outcomes that outstrip competition hit your inbox, but your company either hasn’t started at all or is mired in the discussion. AI, machine learning, and deep learning are sometimes used... Read more
25 Excellent Machine Learning Open Datasets
Your machine learning program is only as good as your training sets. Data sets are an integral part of the quality of your machine learning, but you may not always have access to data behind closed walls or the budget to purchase (or rent) the key. Don’t despair. There... Read more
5 Roadblocks to Getting an ML System in Production
We typically meet an organization’s data science team after they’ve carried out a successful proof of concept. The algorithm they built or acquired produced results that were promising enough to greenlight development of a production ML system. It’s at this point that the immaturity of ML project management often... 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
4 Steps to Start Machine Learning with Computer Vision
In 2012, AlexNet took first place at the ImageNet Large Scale Visual Recognition Challenge, marking the first time a convolutional neural network had won the image classification competition. One more factor that made this achievement much more significant is that AlexNet showed twice the accuracy than the second-place participant.... Read more
Darwin: Machine Learning Beyond Predefined Recipes
The same way a tailored suit feels and looks different from generic options because it actually fits, tailored models perform differently than pre-established boxed algorithms because they are custom-fitted to your data. To answer this need, SparkCognition has developed Darwin™, a machine learning product that automates the building and... Read more
Machine Learning Challenges You Might Not See Coming
There seems to be a skills gap, and a skills misunderstanding, when it comes to Data Science, Engineering, and DevOps as a joint process. At our machine learning consultancy, Infinia ML, we view deployment as a sequential process across teams: (1) Data Science explores data and develops algorithm(s). (2a)... Read more
Identifying Poisonous Mushrooms with Rule Learners
Each year, many people fall ill and sometimes even die from ingesting poisonous wild mushrooms. Since many mushrooms are very similar to each other in appearance, occasionally even experienced mushroom gatherers are poisoned. If simple, clear, and consistent rules were available for identifying poisonous mushrooms, they could save the... Read more
Using NLP and ML to Analyze Legislative Burdens Upon Businesses
The process of legal reasoning and decision making is heavily reliant on information stored in text. Tasks like due diligence, contract review, and legal discovery, that are traditionally time-consuming, can be automated, saving a huge amount of time. This makes the development of approaches that leverage natural language processing... Read more
Why Do Tree Ensembles Work?
Ensembles of decision trees (e.g., the random forest and AdaBoost algorithms) are powerful and well-known methods of classification and regression. We will survey work aimed at understanding the statistical properties of decision tree ensembles, with the goal of explaining why they work. An elementary probabilistic motivation for ensemble methods... Read more