<span>D</span>eep neural networks are the go to algorithm when it comes to image classification. This is partly because they can have arbitrarily large number of trainable parameters. However, this comes at a cost of requiring a large amount of data, which is sometimes not available. I will discuss One Shot <a href="https://hackernoon.com/tagged/learning" target="_blank">Learning</a>, which aims to mitigate such an issue, and how to implement a Neural Net capable of using it ,in PyTorch
In this blog post, we'll be discussing the basics of TensorFlow and how it can be used to create an artificial neural network
NeurIPS 2020 Depth Uncertainty in Neural Networks Meta Review This paper proposes to treat depth of the network as a random variable and marginalize over that to achieve better uncertainty. The authors show that this can be performed efficiently in a single forward pass, and demonstrate improved uncertainty estimates on both regression and classification benchmarks (including corrupted versions and out-of-distribution evaluation). The reviewers initially raised several questions particularly on experimental
A Siamese neural network (SNN) is a type of neural network architecture that contains two or more identical sub-networks with the same parameters and weights. SNNs can be used to build a predictive model using only a few images per class
This blog post is written as a dialogue between two imaginary characters, one of them representing myself (H) and the other a stubborn straw man (S). It is
LSTM networks are recurrent neural models that use memory cells and gating mechanisms to capture long-range dependencies in language, speech, and time-series tasks
Decoding the Power of Bayesian Networks: A Journey into AI's Probabilistic Realm
Traditional deep neural nets (NNs) have shown the state-of-the-art performance in the task of classification in various applications. However, NNs have not considered any types of uncertainty
# Neural Network Smartens Up A Security System - by: - Zoe Skyforest July 3, 2019 It’s all well and good having a security camera recording all the time, but that alone can’t sound the alarm in the event of a crime. Motion sensing is of limited use, often being triggered by unimportant stimuli such as moving shadows or passing traffic. [Tegwyn☠Twmffat] wanted a better security system for the farm, and decided that neural networks would likely do the trick. The main component of the security system is
A recurrent neural network (RNN) is a type of neural network designed to process sequential data by maintaining a "memor