Learn how to estimate model uncertainty in neural networks

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Learn how neural networks work, from layers and forward propagation to output generation, with simple real-world examples

# Recurrent Neural Networks in Tensorflow I Mon 11 July 2016 This is the first in a series of posts about recurrent neural networks in Tensorflow. In this post, we will build a vanilla recurrent neural network (RNN) from the ground up in Tensorflow, and then translate the model into Tensorflow’s RNN API. Edit 2017/03/07: Updated to work with Tensorflow 1.0. ### Introduction to RNNs RNNs are neural networks that accept their own outputs as inputs. So as to not reinvent the wheel, here are a few blog pos

Recurrent neural networks - Topic:Artificial Intelligence - Lexicon & Encyclopedia - What is what? Everything you always wanted to know

Learn how feedforward neural networks learn through backpropagation and gradient descent, how activation functions introduce nonlinearity, why ReLU solves the vanishing gradient problem, and how dropout and batch normalization regularize networks

Implement basic neural networks from scratch using NumPy and then using PyTorch

Get an overview of what is going on inside convolutional neural networks, and what it is that makes them so effective

Convolutional Neural Networks (CNNs) are a type of deep learning model designed to process grid-like data, such as image

How efficiently can we mimic biological spiking process of neurons and synapses, and is CMOS a good choice for neural networks