Category: Neural network

2020-02-17 Martijn van Wezel

The SNNs bio-inspired neural networks are different from conventional neural networks due that the conventional neural networks communicate with numbers. Instead, SNNs communicate through spikes. … Having multiple spikes in a short period can stimulate the neuron to fire. However, if the time periods are to big between spikes, the neuron lose interest, and goes to sleep again.

… one major benefit of a Spiking Neural Networks is the power consumption. A ‘normal’ neural network uses big GPUs or CPUs that draw hundreds of Watts of power. SNN only uses for the same network size just a few nano Watts.

https://martijnvwezel.com/blogs/spiking_neural_networks/

2020-02-13 Tommy Thompson

One of the biggest headlines in AI research for 2019 was the unveiling of AlphaStar – Google DeepMind’s project to create the worlds best player of Blizzard’s real-time strategy game StarCraft II.  After shocking the world in January as the system defeated two high ranking players in closed competition, an updated version was revealed in November that had achieved grandmaster status: ranking among the top 0.15% in Europe’s 90,000 active players.  So let’s look at how AlphaStar works, the underpinning technology and theory that drives it, the truth behind the media sensationalism and how it achieved grandmaster rank in online multiplayer.

https://www.gamasutra.com/blogs/TommyThompson/20200213/358051/How_AlphaStar_Became_a_StarCraft_Grandmaster.php

2019-12 Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola

An interactive deep learning book with code, math, and discussions, based on the NumPy interface.

https://d2l.ai/index.html

2019-11-25 firmai / Derek Snow

“A curated list of repositories with fully functional click-and-run colab notebooks with data, code and description. The code in these repositories are in Python unless otherwise stated.”

Some of the Google Colaboratory Notebooks listed use artificial neural networks. Google Colaboratory Notebooks are Jupyter notebooks that run Python or other code on Google’s cloud for free.

 

2019-08-27  Adam Gaier, Student Researcher and David Ha, Staff Research Scientist, Google Research, Tokyo

When training a neural network to accomplish a given task, be it image classification or reinforcement learning, one typically refines a set of weights associated with each connection within the network. Another approach to creating successful neural networks that has shown substantial progress is neural architecture search, which constructs neural network architectures out of hand-engineered components such as convolutional network components or transformer blocks. It has been shown that neural network architectures built with these components, such as deep convolutional networks, have strong inductive biases for image processing tasks, and can even perform them when their weights are randomly initialized.

https://ai.googleblog.com/2019/08/exploring-weight-agnostic-neural.html

 

2019-07 Jason Brownlee

In this tutorial, you will discover how to create your first deep learning neural network model in Python using Keras.

https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/

 

2019-02-04 Karlijn Willems

This Keras tutorial introduces you to deep learning in Python. (Keras is a neural network library for Python.)

https://www.datacamp.com/community/tutorials/deep-learning-python