Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Fall 2019 Notebook: http://deeplearning.cs.cmu.edu/document/recitation/recitation...
An attempt to create a convenient workspace that makes it possible to work with multiple custom python libraries, while keeping all benefits of Google Colaboratory.
This is a short collection of lessons learned using Colab as my main coding learning environment for the past few months. Some tricks are Colab specific, others as general Jupyter tips, and still more are filesystem related, but all have proven useful for me.
Now you can develop deep learning applications with Google Colaboratory -on the free Tesla K80 GPU- using Keras, Tensorflow and PyTorch. Hello! I will show you how to use Google Colab, Google’s free…
Read top stories published by Artists and Machine Intelligence. AMI is a program at Google that brings together artists and engineers to realize projects using Machine Intelligence. Works are developed together alongside artists’ current practices and shown at galleries, biennials, festivals, or online.
Humans excel at solving a wide variety of challenging problems, from low-level motor control through to high-level cognitive tasks. Our goal at DeepMind is to create artificial agents that can achieve a similar level of performance and generality. Like a human, our agents learn for themselves to achieve successful strategies that lead to the greatest long-term rewards.
Neural networks are the workhorse of many of the algorithms developed at DeepMind. For example, AlphaGo uses convolutional neural networks to evaluate board positions in the game of Go and DQN and Deep Reinforcement Learning algorithms use neural networks to choose actions to play at super-human level on video games. This post introduces some of our latest research in progressing the capabilities and training procedures of neural networks called Decoupled Neural Interfaces using Synthetic Gradients. This work gives us a way to allow neural networks to communicate, to learn to send messages between themselves, in a decoupled, scalable manner paving the way for multiple neural networks to communicate with each other or improving the long term temporal dependency of recurrent networks.