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 Deep Learning Explainable Defect Detection Using Convolutional Neural Networks: Case Study Train object detection model without having any bounding boxes labels. This post shows the power of Explainable AI. Olga Chernytska Dec 12, 2021 13 min read Share Image by Author Despite being extremely accurate, neural networks are not that widely
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Convolutional Neural Networks (CNNs), have been very popular in the last decade or so. CNNs have been used in multiple applications like image recognition, image classification, facial recognition, neural style transfer etc. CNN’s have been extremely successful in handling these kind of problems. How do they work? What makes them so successful? What is the
Recurrent Neural Networks (RNNs) are a class of neural networks designed for sequential data. Unlike feedforward networks that process each input
Neural networks are the mathematical brains behind modern AI—think of them as simplified versions of how your actual brain processes information
Recurrent neural networks (RNNs) use sequential data to solve common temporal problems seen in language translation and speech recognition
Neural networks and rule-based AI? https://bdtechtalks.com/2019/06/05/mit-ibm-hybrid-ai/ As was noted ReLU activation functions are literal switches. They switch together webs of dot products into a linear projection
Distill Attention and Augmented Recurrent Neural Networks Sept. 8 2016 Citation: Olah & Carter, 2016 Recurrent neural networks are one of the staples of deep learning, allowing neural networks to work with sequences of data like text, audio and video. They can be used to boil a sequence down into a high-level understanding, to annotate sequences, and even to generate new sequences from scratch! The basic RNN design struggles with longer sequences, but a special variant— “long short-term memory
Conventional applications of neural networks usually predict a single value as a function of given inputs. In forecasting, for example, a standard objective is to predict the future value of some entity of interest on the basis of a time series of past measurements or observations. Typical training schemes aim to minimise the sum of squared deviations between predicted and actual values (the 'targets'), by which, ideally, the network learns the conditional mean of the target given the input. If the underlyi
Neural networks explained. Learn how neural networks work. Discover common architectures of neural networks and applications