Welcome back! Rust is a very powerful programming language used by tons of different companies, including Google! Let’s go ahead and see what projects Google actually uses Rust for. Now, the process…
Principal component analysis(PCA) is one of the key algorithms that are part of any machine learning curriculum. Initially created in the early 1900s, PCA is a fundamental algorithm to understand…
A paper by DeepMind scientist triggered much debate about the path to artificial intelligence. Here, we'll try to draw the line between theory and practice.
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.
In December 2017, researchers at Google and MIT published a provocative research paper about their efforts into “learned index structures”. The research is quite exciting, as the authors state in the…
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…
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.
by William Slawski - "... I decided to take a closer look at some of the companies that Google has purchased. I’m glad I did. I came across a couple of papers I hadn’t seen before ..."