Artificial intelligence, machine learning and deep learning are some of the biggest buzzwords around today. This guide provides a simple definition for deep learning that helps differentiate it ...

Deep learning represents a powerful advancement in AI and machine learning, providing the ability to automatically learn from large datasets and achieve remarkable results in a wide range of fields.

Deep learning is a particular subset of machine learning (the mechanics of artificial intelligence). While this branch of programming can become very complex, it started with a very simple ...

Deep learning vs. machine learning: what's the difference between the two? We provide a simplified explanation of both AI-based technologies.

Example Google: The deep learning trailblazer. Alphabet (GOOG-0.37%) (GOOGL-0.37%) and its Google arm have been superstars of deep learning for many years. You'll find neural networks behind the ...

An AI machine learning architecture employed in "neural networks." Emerging in the 2010s, deep learning is used in all forms of AI such as computer vision, self-driving cars, natural language ...

Deepfakes are simple to make. A simple overview of the artificial intelligence (AI) behind deepfakes: Generative Adversarial Networks (GANs), Encoder-decoder pairs and First-Order Motion Models.

Even in TensorFlow 1.12, the official Get Started with TensorFlow tutorial uses the high-level Keras API embedded in TensorFlow, tf.keras.By contrast, the TensorFlow Core API requires working with ...

In general, classical (non-deep) machine learning algorithms train and predict much faster than deep learning algorithms; one or more CPUs will often be sufficient to train a classical model.