On the intuition behind deep learning & GANs — towards a fundamental understanding

@tachyeonz : A generative adversarial network (GAN) is composed of two separate networks – the generator and the discriminator. It poses the unsupervised learning problem as a game between the two. In this post we will see why GANs have so much potential, and frame GANs as a boxing match between two opponents.

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Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)

@tachyeonz : In 2014, Ian Goodfellow and his colleagues at the University of Montreal published a stunning paper introducing the world to GANs, or generative adversarial networks.

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The Emergence of Modular Deep Learning

@tachyeonz : Deep Learning compared to other Machine Learning methods is remarkably modular. This modularity gives it unprecedented capabilities that places Deep Learning head and shoulders above any other conventional Machine Learning approach.

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Astronomers explore uses for AI-generated images

@tachyeonz : AI-generated images of galaxies (left, lower of each pair) and volcanoes. Volcanoes, monasteries, birds, thistles: the varied images in Jeff Clune’s research paper could be his holiday snaps. In fact, the pictures are synthetic.

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Game Theory Reveals the Future of Deep Learning

@tachyeonz : If you’ve been following my articles up to now, you’ll begin to perceive, what’s apparent to many advanced practitioners of Deep Learning (DL), is the emergence of Game Theoretic concepts in the design of newer architectures. This makes intuitive sense for two reasons.

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Tags : adversarial networks, deep learning, discriminant, game theory, gans, neural networks, unsupervised learning, yann le cun, z

Published On:January 05, 2017 at 01:17AM

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These Were The Best Machine Learning Breakthroughs Of 2016

@tachyeonz : What were the main advances in machine learning/artificial intelligence in 2016? originally appeared on Quora: the knowledge sharing network where compelling questions are answered by people with unique insights.

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Tags : 2016, breakthroughs, cntk, deep learning, dntk, gans, generative adversarial, innovations, lstm, machine learning, mxnet, neural networks, nips2016, probabilistic models, probabilistic programming, statistics, wavenet, z

Published On:January 04, 2017 at 04:25PM

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Google teaches “AIs” to invent their own crypto and avoid eavesdropping

@tachyeonz : Google Brain has created two artificial intelligences that evolved their own cryptographic algorithm to protect their messages from a third AI, which was trying to evolve its own method to crack the AI-generated crypto.

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Tags : analytics, artificial intelligence, data science, deep learning, gans, generative adversarial, machine learning, neural networks, z

Published On:January 04, 2017 at 03:59PM

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Deep Learning 2016: The Year in Review

@tachyeonz : In order to understand trends in the field, I find it helpful to think of developments in deep learning as being driven by three major frontiers that limit the success of artificial intelligence in general and deep learning in particular.

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Tags : deep learning, elon musk, gans, generative adversarial, intel, karpathy, nervana, neural networks, nvidia, open ai, reinforcement learning, research paper, russ salakhutdinov, technology, yann le cun, z

Published On:January 01, 2017 at 05:29PM

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Apple leaps into AI research with improved simulated + unsupervised learning

@tachyeonz : Corporate machine learning research may be getting a new vanguard in Apple. Six researchers from the company’s recently formed machine learning group published a paper that describes a novel method for simulated + unsupervised learning.

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Tags : apple, artificial intelligence, gans, generative adversarial, m, machine learning, networks, news, research paper, russ salakhutdinov, simulated, simulated gans, technology, unsupervised learning

Published On:December 27, 2016 at 06:45PM

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NIPS 2016 Review, Day 2

@tachyeonz : Why good morning again, fellow machine learners. It’s another day at NIPS, and what a grueling experience. The sessions ran from 9am to 9pm last night, and I was there for most of it! (Check out my NIPS 2016 Review, Day 1 for the low-down on yesterday’s action.) Ok, let’s get crackin’.

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Tags : gans, generative adversarial, gradient descent, kyle cranmer, m, models, nips2016, pgm, probabilistic graphical, sgd, stochastic

Published On:December 26, 2016 at 05:13PM

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NIPS 2016 Review, Day 1

@tachyeonz : Ever the scientists, the two organizers justified their choice on the program committee by maintaining that they want to grow the number submissions while decreasing bias and variance. They treated the problem with unknown ground truth of what the “best papers” were,

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Tags : artificial intelligence, cnn, conference, convolution neural net, deep learning, gans, lstm, m, machine learning, meta learning, meta models, nips2016, phased lstm, recurrent neuralnet, reinforcement learning, rnn, time series data, unsupervised learning, yann le cun

Published On:December 25, 2016 at 07:26PM

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