Both machine learning and deep learning discover patterns in data, but involve dramatically different techniques
Stories by Martin Heller
From performance to programmability, the right database makes all the difference. Here are 12 key questions to help guide your selection
Using a machine learning model’s own predictions on unlabeled data to add to the labeled data set sometimes improves accuracy, but not always
AutoML frameworks and services eliminate the need for skilled data scientists to build machine learning and deep learning models
Unsupervised learning is used mainly to discover patterns and detect outliers in data today, but could lead to general-purpose AI tomorrow
Supervised learning turns labeled training data into a tuned predictive model
Reinforcement learning uses rewards and penalties to teach computers how to play games and robots how to perform tasks independently
Machine learning uses algorithms to turn a data set into a predictive model. Which algorithm works best depends on the problem
Easy to use and widely supported, Keras makes deep learning about as simple as deep learning can be
GitHub is the host with the most for open source projects and programmers who want to share and collaborate on code. Here’s why
Domo vs. Power BI vs. Qlik Sense vs. QuickSight vs. Tableau: Self-service BI has become the go-to tool for agile, fluid business decisions. Here’s how to select the right platform for your business.
Looking to avoid monthly cloud sticker shock? A cloud cost management strategy that makes use of containers, capacity pre-purchases and more will help you contain runaway cloud spending.
Not every problem can be solved by machine learning, and not every company is poised to apply AI. Here’s how to know whether your IT organization is ready to reap the benefits of artificial intelligence.
Big data, machine learning, data science — the data analytics revolution is evolving rapidly. Keep your BA/BI pros and data scientists ahead of the curve with the latest technologies and strategies for data analysis.
GPUs in the cloud put the predictive power of deep neural networks within reach of every developer