Convolutional Neural Networks are deep models that use convolution, pooling, and advanced architectures to extract hierarchical features from structured data
Test your understanding of Convolutional Neural Networks with tiered multiple-choice questions on TheoremPath
Discover why mathematicians believe neural networks can learn. Explore the history of neural networks, from early failures to groundbreaking theorems like Stone-Weierstrass
Skip to content Publish AI, ML & data-science insights to a global community of data professionals. Sign in Submit an Article Toggle Mobile Navigation Toggle Search Search Artificial Intelligence Pruning Neural Networks Neural networks can be made smaller and faster by removing connections or nodes Rohit Bandaru Sep 1, 2020 5 min read Share Much of the success of deep learning has come from building larger and larger neural networks. This allows these models to perform better on various tasks, but also make
Read our posts about Convolutional Neural Networks (CNNs), a class of deep neural networks designed for tasks involving visual data, such as image and video recognition, object detection, and image classification
Transformers are Graph Neural Networks 12.Sep.2020 . 12 min read My engineering friends often ask me: deep learning on graphs sounds great, but are there any real applications? While Graph Neural Networks are used in recommendation systems at Pinterest , Alibaba and Twitter , a more subtle success story is the Transformer architecture , which has taken the NLP world by storm . Through this post, I want to establish a link between Graph Neural Networks (GNNs) and Transformers. I'll talk about the intuitions
NeuPy is a Python library for Artificial Neural Networks. NeuPy supports many different types of Neural Networks from a simple perceptron to deep learning models
What is an RNN? ✓ Learn how Recurrent Neural Networks process sequential data with memory & BPTT ✓ LSTM, GRU, use cases, benefits & challenges ► Read more
Posts about Neural Networks written by Bartosz Milewski
Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network