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2. List of programming languages for artificial intelligence

   en.wikipedia.org/wiki/List_of_programming...

   Python is a high-level, general-purpose programming language that is popular in artificial intelligence. [1] It has a simple, flexible and easily readable syntax. [ 2 ] Its popularity results in a vast ecosystem of libraries , including for deep learning , such as PyTorch , TensorFlow , Keras , Google JAX .

3. MIT App Inventor - Wikipedia

   en.wikipedia.org/wiki/MIT_App_Inventor

   Its target is primarily children and students studying computer programming, similar to Scratch. The web interface consists of a graphical user interface (GUI) very similar to Scratch and StarLogo , allowing users to drag-and-drop visual objects to create an application that can be tested on Android and iOS devices and compiled to run as an ...

4. List of educational programming languages - Wikipedia

   en.wikipedia.org/wiki/List_of_educational...

   It introduces popular programming techniques along with robotics and artificial intelligence. The robot can be programmed in Arabic, Chinese, Dutch, German, English and Swedish. Scratch is a visual language with the goal of teaching programming concepts to children by allowing them to create projects such as games, videos, and music. It does ...

5. Elements of AI - Wikipedia

   en.wikipedia.org/wiki/Elements_of_AI

   Elements of AI is a massive open online course (MOOC) teaching the basics of artificial intelligence. [1] The course, originally launched in 2018, is designed and organized by the University of Helsinki and learning technology company MinnaLearn . [ 2 ]

6. Machine learning - Wikipedia

   en.wikipedia.org/wiki/Machine_learning

   Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]

7. Transformer (deep learning architecture) - Wikipedia

   en.wikipedia.org/wiki/Transformer_(deep_learning...

   The plain transformer architecture had difficulty converging. In the original paper [1] the authors recommended using learning rate warmup. That is, the learning rate should linearly scale up from 0 to maximal value for the first part of the training (usually recommended to be 2% of the total number of training steps), before decaying again.