AI News, Crash Course in Recurrent Neural Networks for Deep Learning
- On Wednesday, June 6, 2018
- By Read More
Crash Course in Recurrent Neural Networks for Deep Learning
There is another type of neural network that is dominating difficult machine learning problems that involve sequences of inputs called recurrent neural networks.
powerful type of Recurrent Neural Network called the Long Short-Term Memory Network has been shown to be particularly effective when stacked into a deep configuration, achieving state-of-the-art results on a diverse array of problems from language translation to automatic captioning of images and videos.
In this post you will get a crash course in recurrent neural networks for deep learning, acquiring just enough understanding to start using LSTM networks in Python with Keras.
Support for sequences in neural networks is an important class of problem and one where deep learning has recently shown impressive results State-of-the art results have been using a type of network specifically designed for sequence problems called recurrent neural networks.
This allows the cyclic graph of a recurrent neural network to be turned into an acyclic graph like a classic feed-forward neural network, and Backpropagation can be applied.
When Backpropagation is used in very deep neural networks and in unrolled recurrent neural networks, the gradients that are calculated in order to update the weights can become unstable.
This problem is alleviated in deep multilayer Perceptron networks through the use of the Rectifier transfer function, and even more exotic but now less popular approaches of using unsupervised pre-training of layers.
In recurrent neural network architectures, this problem has been alleviated using a new type of architecture called the Long Short-Term Memory Networks that allows deep recurrent networks to be trained.
As such it can be used to create large (stacked) recurrent networks, that in turn can be used to address difficult sequence problems in machine learning and achieve state-of-the-art results.
A unit operates upon an input sequence and each gate within a unit uses the sigmoid activation function to control whether they are triggered or not, making the change of state and addition of information flowing through the unit conditional.
There are three types of gates within a memory unit: Each unit is like a mini state machine where the gates of the units have weights that are learned during the training procedure.
- On Thursday, June 27, 2019
Recurrent Neural Networks - Ep. 9 (Deep Learning SIMPLIFIED)
Our previous discussions of deep net applications were limited to static patterns, but how can a net decipher and label patterns that change with time?
Neural Program Learning from Input-Output Examples
Most deep learning research focuses on learning a single task at a time - on a fixed problem, given an input, predict the corresponding output. How should we ...
Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka
TensorFlow Training - ) This Edureka "Neural Network Tutorial" video (Blog: will .
Neural Networks Learn Logic Gates?
Install Tensorflow first, then keras. Follow instructions here: Tensorflow: keras: I installed tf and .
Recurrent Neural Networks (RNN) | RNN LSTM | Deep Learning Tutorial | Tensorflow Tutorial | Edureka
TensorFlow Training - ) This Edureka Recurrent Neural Networks tutorial video (Blog: ..
Lecture 6 | Training Neural Networks I
In Lecture 6 we discuss many practical issues for training modern neural networks. We discuss different activation functions, the importance of data ...
Neural Networks 6: solving XOR with a hidden layer
Recurrent Neural Network - The Math of Intelligence (Week 5)
Recurrent neural networks let us learn from sequential data (time series, music, audio, video frames, etc ). We're going to build one from scratch in numpy ...
Recurrent Neural Network Genetic Learning to play Agairo in MultiWorld
Recurrent Neural Network Genetic Learning to play More detailed: Neural net config 16, 18, 8,4 three layers , input, 2 hidden and output Number of dendrites or ...
But what *is* a Neural Network? | Chapter 1, deep learning
Subscribe to stay notified about new videos: Support more videos like this on Patreon: Special .