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Meta-Learning: Learning to Learn Fast
Meta-learning, also known as “learning to learn”, intends to design models that can learn new skills or adapt to new environments rapidly with a few training examples. There are three common approaches: 1) learn an efficient distance metric (metric-based); 2) use (recurrent) network with external or internal memory (model-based); 3) optimize the model parameters explicitly for fast learning (optimization-based).
machine-learning  deep-learning 
yesterday by doneata
Colorizing and Restoring Old Images with Deep Learning
Jason Antic's DeOldify deep learning project not only colorizes images but also restores them with stunning results. Learn how in this FloydHub interview.
machinelearning  python  deep-learning  tutorial 
yesterday by cychong47
Open AI – Spinning Up
Welcome to Spinning Up in Deep RL! This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL).

For the unfamiliar: reinforcement learning (RL) is a machine learning approach for teaching agents how to solve tasks by trial and error. Deep RL refers to the combination of RL with deep learning.

This module contains a variety of helpful resources, including:

a short introduction to RL terminology, kinds of algorithms, and basic theory, an essay about how to grow into an RL research role, a curated list of important papers organized by topic, a well-documented code repo of short, standalone implementations of key algorithms, and a few exercises to serve as warm-ups.
reinforcement-learning  deep-learning  tutorial 
3 days ago by doneata

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