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Mitigating the risks of the AI black box
If we don’t understand how machine learning works, how can we trust it? Increasing model transparency creates risks as well as rewards
Artificial intelligence predictions for 2020
Big changes in machine learning applications, tools, techniques, platforms, and standards are on the horizon
6 best programming languages for AI development
Which programming language should you pick for your machine learning or deep learning project? These are your best options
Handicapping the AI modeling horse race
AI has become a core focus for application developers everywhere. It’s as hot in the consumer space as it is in business, industry, research, and government
Google previews site for sharing machine learning experiments
TensorBoard.dev allows you to upload and share TensorBoards and visualize model graphs and metrics
Google Cloud launches TensorFlow Enterprise
Google Cloud service combines the TensorFlow machine learning platform with enterprise support and managed services
Qubole review: Self-service big data analytics
Cloud-native data platform puts Spark, Presto, Hive, and Airflow at your fingertips, while controlling your cloud spending
AI gets real (sort of) in the enterprise
Turns out AI isn’t magic pixie dust to sprinkle over legacy processes and legacy tech, but a fundamental rethinking of how to do business
Artificial intelligence today: What’s hype and what’s real?
Two decades into the AI revolution, deep learning is becoming a standard part of the analytics toolkit. Here’s what it means
Semi-supervised learning explained
Using a machine learning model’s own predictions on unlabeled data to add to the labeled data set sometimes improves accuracy, but not always
IBM Trusted AI toolkits for Python combat AI bias
IBM has released Python toolkits for identifying and mitigating against bias in training data and machine learning models
PyTorch vs. TensorFlow: How to choose
If you actually need a deep learning model, PyTorch and TensorFlow are the two leading options
Automated machine learning or AutoML explained
AutoML frameworks and services eliminate the need for skilled data scientists to build machine learning and deep learning models
5 machine learning tools to ease software development
AI-driven development tools that provide code auto-completion, code vulnerability detection, and even cutting-edge code generation
The best machine learning and deep learning libraries
TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models
TensorFlow 2 review: Easier machine learning
Now more platform than toolkit, TensorFlow has made strides in everything from ease of use to distributed training and deployment
Supervised learning explained
Supervised learning turns labeled training data into a tuned predictive model
Reinforcement learning explained
Reinforcement learning uses rewards and penalties to teach computers how to play games and robots how to perform tasks independently
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