Read articles about Graph Neural Networks in Towards Data Science - the world’s leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals
Explore 11 artificial intelligence milestones related to neural networks, from the 1940s to today
Recurrent Neural Networks (RNNs) process sequential data through cyclic connections that retain context and capture temporal dependencies for varied applications
In the last few decades, neural networks have evolved from an academic curiosity into a vast “deep learning” industry. Deep learning uses neural networks, a data structure design loosely inspired by the layout of biological neurons. These neural networks are constructed in layers, and the inputs from one layer are connected to the outputs of the next layer
Michael Nielsen's online book on Neural Networks and Deep Learning. This book is a neural networks and deep learning tutorial
Thomas Shultz, Professor @ McGill University - Learning & development - Neural networks - Memory - Evolution - Cognitive dissonance - Problem solving - Decision making - Commentaries - Blog posts Research highlights - Resolving the St. Petersburg paradox - Spread of innovation in wild birds - Resolving Rogers' paradox - Evolution of ethnocentrism - Shape of development - Connectionist modeling - Neural networks - Symbolic modeling - Causal reasoning - Moral reasoning - Theory of mind - Development of hum
Explore neural networks in psychology, their applications in cognitive modeling, clinical diagnosis, and research, and their impact on the field's future
Jeremy Jordan Sign in Machine Learning Neural networks: representation. Jeremy Jordan 28 Jun 2017 • 7 min read This post aims to discuss what a neural network is and how we represent it in a machine learning model. Subsequent posts will cover more advanced topics such as training and optimizing a model, but I've found it's helpful to first have a solid understanding of what it is we're actually building and a comfort with respect to the matrix representation we'll use. Prerequisites: Read my post on
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