Deep Learning

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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

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Artificial intelligence predictions for 2020

Big changes in machine learning applications, tools, techniques, platforms, and standards are on the horizon

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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

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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

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Google previews site for sharing machine learning experiments

TensorBoard.dev allows you to upload and share TensorBoards and visualize model graphs and metrics

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Google Cloud launches TensorFlow Enterprise

Google Cloud service combines the TensorFlow machine learning platform with enterprise support and managed services

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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

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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

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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

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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

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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

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PyTorch vs. TensorFlow: How to choose

If you actually need a deep learning model, PyTorch and TensorFlow are the two leading options

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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

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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

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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

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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

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Supervised learning explained

Supervised learning turns labeled training data into a tuned predictive model

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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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