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  2. fast.ai - Wikipedia

    en.wikipedia.org/wiki/Fast.ai

    In the fall of 2018, fast.ai released v1.0 of their free open-source library for deep learning called fastai (without a period), sitting atop PyTorch. Google Cloud was the first to announce its support. [6] This open-source framework is hosted on GitHub and is licensed under the Apache License, Version 2.0. [7] [8]

  3. Fast Artificial Neural Network - Wikipedia

    en.wikipedia.org/wiki/Fast_Artificial_Neural_Network

    Trained artificial neural networks can be stored as .net files to quickly saved and load ANNs for future use or future training. This allows dividing the training into multiple smaller steps, which can be useful when dealing with large training datasets or large neural networks.

  4. Jeremy Howard (entrepreneur) - Wikipedia

    en.wikipedia.org/wiki/Jeremy_Howard_(entrepreneur)

    Jeremy Howard (born 13 November 1973) is an Australian data scientist, entrepreneur, and educator. [1]He is the co-founder of fast.ai, where he teaches introductory courses, [2] develops software, and conducts research in the area of deep learning.

  5. LeNet - Wikipedia

    en.wikipedia.org/wiki/LeNet

    LeNet-5 architecture (overview). LeNet is a series of convolutional neural network structure proposed by LeCun et al.. [1] The earliest version, LeNet-1, was trained in 1989.In general, when "LeNet" is referred to without a number, it refers to LeNet-5 (1998), the most well-known version.

  6. Latent diffusion model - Wikipedia

    en.wikipedia.org/wiki/Latent_Diffusion_Model

    The Latent Diffusion Model (LDM) [1] is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) [2] group at LMU Munich. [3]Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian) on training images.

  7. Deep learning - Wikipedia

    en.wikipedia.org/wiki/Deep_learning

    Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.

  8. Dog Trainer Reveals Big Mistake People Make When Teaching ...

    www.aol.com/dog-trainer-reveals-big-mistake...

    Dog trainer and expert Steve Del Savio shared a video on Tuesday, November 19th and revealed one big mistake that most people make when teaching their dogs the 'place' command as well as how to ...

  9. Generative adversarial network - Wikipedia

    en.wikipedia.org/wiki/Generative_adversarial_network

    A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. [1] [2] The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. [3]