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.
One of the hardest concepts to grasp when learning about Convolutional Neural Networks for object detection is the idea of anchor boxes. It is also one of the most important parameters you can tune…
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 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…
Unlike task-specific algorithms, Deep Learning is a part of Machine Learning family based on learning data representations. With massive amounts of computational power, machines can now recognize…
Ever since graduation, people have been asking me: “What’s now?” My answer has been an unequivocal: “I don’t know.” I used to think that by the time I finish...
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…
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.