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Neural collaborative filtering (NCF) is a type of recommendation system that uses neural networks to predict user prefer
Hironobu SUZUKI @ InterDB > Part 2: Recurrent Neural Networks Part 2: Recurrent Neural Networks Recurrent neural networks (RNNs) were invented to handle time series data. In the AI field, time series data includes both numerical sequences, such as stock prices and temperatures, and natural language sequences, such as sentences where each word is connected to the others. Type of RNNs There are four main types of RNNs, each with different input and output structures: (1) Many-to-One A many-to-one RNN takes mu
Explore a comprehensive survey on deep neural networks applied to tabular data, highlighting methodologies, performance, and practical implications
Combined interactive demo showing neural network prediction across multiple financial scenarios: function approximation, NASDAQ stock, and Forex currency data
Busting the biggest public misconception in AI
You have a very wide range of choices if you use locality sensitive hashing to select weights in a neural network. Not only various select from a pool strategies, also various switching scopes (eg. input weights to a neu
What a neural network is in plain terms: how neurons, layers, weights, and activations work, and how a network learns via training and backpropagation
# Credit Assignment in Spiking Neural Networks The problem of spatial and temporal credit assignment in RNNs are solved through backpropagating errors in the unrolled RNN. Algorithmic solutions to RNNs have 2 challenges in Spiking Neural Networks . First, spiking neurons have $S(U(t)) = \(\Theta(U(t) - \theta)\). Their derivative is zero everywhere except at \(U = \theta\), where it is ill-defined. This binary spiking non-linearity stops gradients from flowing, and makes gradient-based optimization unsuit
Blog Topics Advertise Join Newsletter The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP) Natural language processing has made incredible advances through advanced techniques in deep learning. Learn about these powerful models, and find how close (or far away) these approaches are to human-level understanding. By Kevin Vu , Exxact Corp on June 29, 2020 in Deep Learning , LSTM , NLP , Transfer Learning , Transformer , Trends --> comments Humans have a lot of senses, and yet