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The universal approximation theorem states that a feed-forward network with a single hidden layer containing a finite number of neurons can solve any given problem to arbitrarily close accuracy as long as you add enough parameters.

The deep convolutional network, inspired by Hubel and Wiesel’s seminal work on early visual cortex, uses hierarchical layers of tiled convolutional filters to mimic the effects of receptive fields, thereby exploiting the local spatial correlations present in images.

RNNs process an input sequence one element at a time, maintaining in their hidden units a ‘state vector’ that implicitly contains information about the history of all the past elements of the sequence.

“I feel like a significant percentage of Deep Learning breakthroughs ask the question “how can I reuse weights in multiple places?”– Recurrent (LSTM) layers reuse for multiple timesteps– Convolutional layers reuse in multiple locations.– Capsules reuse across orientation.“ — Trask True intelligence will require independent learning strategies.

$$\min_{\theta_g} \max_{\theta_d} [{\rm IE_{x\sim p_{data}(x)}} [log D_{\theta_d}(x)] + {\rm IE_{z\sim p_z(z)}} [log(1 - D_{\theta_d}(G_{\theta_g}(z)))]]$$ “What I cannot create, I do not understand.“ — Richard Feynman This framework corresponds to a minimax two-player game.

And I think the key to doing transfer learning will be the acquisition of conceptual knowledge that is abstracted away from perceptual details of where you learned it from.“ — Demis Hassabis Reinforcement learning (RL) studies how an agent can learn how to achieve goals in a complex, uncertain environment.

Here’s an informal definition of the universal intelligence of agent `\pi` $$\Upsilon(\pi) := \sum\limits_{\mu \in E} 2^{-K(\mu)} V^{\pi}_{\mu}$$ “Intelligence measures an agent’s ability to achieve goals in a wide range of environments.“ — Shane Legg “Evolution is a slow learning algorithm that with the sufficient amount of compute produces a human brain.“ — Wojciech Zaremba Evolution and neural networks proved a potent combination in nature.

“… evolution — whether biological or computational — is inherently creative, and should routinely be expected to surprise, delight, and even outwit us.“ — The Surprising Creativity of Digital Evolution, Lehman et al.

The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples.

A meta-learning algorithm takes in a distribution of tasks, where each task is a learning problem, and it produces a quick learner — a learner that can generalize from a small number of examples.

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