Deep Learning
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5 AI startups out to change the world
Deep learning is solving challenging problems in industries as diverse as retail, manufacturing, and agriculture. These companies are leading the way.
How the cloud and big compute are remaking HPC
High-performance computing projects require massive quantities of compute resources. Pairing simulation and specialized hardware with the cloud powers the breakthroughs of the future.
State of AI report finds AI is now core to business success
Appen survey of business leaders and data scientists indicates that AI initiatives are delivering business value but challenges still exist.
5 AI startups leading MLops
From data preparation and training to model deployment and beyond, these companies offer state-of-the-art platforms for managing the entire machine learning lifecycle.
IBM Python toolkit measures AI uncertainty
IBM’s Uncertainty Qualification 360 is an open source library of Python algorithms for quantifying, estimating, and communicating the uncertainty of machine learning models.
3 AI startups revolutionizing NLP
Deep learning has yielded amazing advances in natural language processing. Tap into the latest innovations with Explosion, Huggingface, and John Snow Labs.
How AI can enhance customer experience
G&J Pepsi and Zipline turn to data science and machine learning to get the right products to the right locations at the right time.
Dataiku review: Data science fit for the enterprise
Dataiku’s end-to-end machine learning platform combines visual tools, notebooks, and code to address the needs of data scientists, data engineers, business analysts, and AI consumers.
4 key tests for your AI explainability toolkit
Enterprise-grade explainability solutions provide fundamental transparency into how machine learning models make decisions, as well as broader assessments of model quality and fairness. Is yours up to the job?
8 ways to jump-start your machine learning
From exploratory data analysis to automated machine learning, look to these techniques to get your data science project moving — and to build better models.
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What we just learned about data science — and what’s next
The past 12 months have revealed how valuable data science can be while also exposing its limitations. Expect big advances in the year to come.
Battling bias and other toxicities in natural language generation
Despite numerous and concerted efforts to train NLG systems to generate content without offensive elements, success is still elusive.
Review: AWS AI and Machine Learning stacks up, and up
Amazon Web Services provides an impressively broad and deep set of machine learning and AI services, rivaling Google Cloud and Microsoft Azure.
Review: Microsoft Azure AI and Machine Learning aims for the enterprise
Microsoft Azure combines a wide range of cognitive services and a solid platform for machine learning that supports automated ML, no-code/low-code ML, and Python-based notebooks.
Why enterprises are turning from TensorFlow to PyTorch
The deep learning framework PyTorch has infiltrated the enterprise thanks to its relative ease of use. Three companies tell us why they chose PyTorch over Google’s renowned TensorFlow framework.
Review: DataRobot aces automated machine learning
DataRobot’s end-to-end AutoML suite not only speeds up the creation of accurate models, but can combine time series, images, geographic information, tabular data, and text in a single model.
Apple releases TensorFlow fork with speedups for M1 Macs
Apple says the M1-compiled version of TensorFlow delivers several times faster performance on a number of benchmarks, while running existing TensorFlow scripts as-is
Today’s data science roles won’t exist in 10 years
AutoML is poised to turn developers into data scientists — and vice versa. Here’s how AutoML will radically change data science for the better.
Review: Google Cloud AI lights up machine learning
Google Cloud AI and Machine Learning Platform is missing some pieces, and much is still in beta, but its scope and quality are second to none.
