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.

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 ...

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

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 ...

Another deep-learning pioneer, Jürgen Schmidhuber at the Dalle Molle Institute for Artificial Intelligence Research in Manno, Switzerland, thinks along similar lines.

In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary ...