The purpose of deep learning is to learn a representation of high dimensional and noisy data using a sequence of differentiable functions, i.e., geometric transformations, that can perhaps be used…
by Computer Vision Department of NTRLab Suppose we are given a set of distinct points P = {(xi, yi) ∈ ℝm ×ℝ}i=1,...,n which we regard as a set of test samples xi ∈ ℝm with known answers yi ∈ ℝ.
Next time you’re at King’s Cross station, take a moment to think about this. Just yards from where you’re standing, the world’s most advanced artificial intelligence (AI) technology is being developed — by a London company called DeepMind.
I teach deep learning both for a living (as the main deepsense.ai instructor, in a Kaggle-winning team1) and as a part of my volunteering with the Polish Chi...
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.
The codebase contains a replica of the AlphaZero methodology, built in Python and Keras. Gain a deeper understanding of how AlphaZero works and adapt the code to plug in new games.
It is of course an outdated model of how the neurons actually work. The current neural network research and development is more driven by mathematically techniques that ensure continuity and…
Geoffrey Hinton has finally expressed what many have been uneasy about. In a recent AI conference, Hinton remarked that he was “deeply suspicious” of back-propagation, and said “My view is throw it…
Part I: Intuition (you are reading it now) Part II: How Capsules Work Part III: Dynamic Routing Between Capsules Part IV: CapsNet Architecture (coming soon) Quick announcement about our new…
Quite a few people have asked me recently about choosing a GPU for Machine Learning. As it stands, success with Deep Learning heavily dependents on having the right hardware to work with. When I was…
In the second part of our "A Mathless Guide to Neural Networks," we’ll take a look at why high-quality, labeled data is so important, where it comes from,..
You’ve framed your problem, prepared your datasets, designed your models and revved up your GPUs. With bated breath, you start training your neural network, hoping to return in a few days to great…
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.