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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
Deep learning frameworks: PyTorch vs. TensorFlow
If you actually need a deep learning model, PyTorch and TensorFlow are both good choices
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
Natural language processing explained
Deep learning has improved machine translation and other NLP tasks by leaps and bounds
Deep learning explained
Deep neural networks can solve the most challenging problems, but require abundant computing power and massive amounts of data
Machine learning explained
Able to learn from data, machine learning algorithms can solve problems that are too complex to solve with conventional programming
Machine learning algorithms explained
Machine learning uses algorithms to turn a data set into a model. Which algorithm works best depends on the problem
How to model time-series anomaly detection for IoT
Machines fail. By creating a time-series prediction model from historical sensor data, you can know when that failure is coming
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