# Attention and Augmented Recurrent Neural Networks 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” networks [1] —can even work with these. Such models
# Fully Connected Neural Network Algorithms In the previous post , we looked at Hessian-free optimization, a powerful optimization technique for training deep neural networks. In the next several, I'm going to look into implementation details of deep convolutional networks. I'm going to begin by reviewing simple fully connected neural networks, re-deriving the backpropagation algorithm for computing the error gradient, and using a clever method ( Pearlmutter, 1993 ) to find an algorithm for computing the
Product About Research The Neural Geometry Series A series about mapping the inner geometry of neural networks: the multidimensional structures in models' activations, the computations that those structures support, and new methods that let us recover, understand, and control them. The World Inside Neural Networks How neural geometry will unlock understanding and control of AI Neural networks develop rich geometric structure in their activations, mirroring the structure of the world they are trained on: day
Product About Research The Neural Geometry Series A series about mapping the inner geometry of neural networks: the multidimensional structures in models' activations, the computations that those structures support, and new methods that let us recover, understand, and control them. The World Inside Neural Networks How neural geometry will unlock understanding and control of AI Neural networks develop rich geometric structure in their activations, mirroring the structure of the world they are trained on: day
Hello! I have a problem with the computation of the cost in the function # GRADED FUNCTION: compute_cost of the last Assignment of Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Feed-Forward networks: (Fig.1) A feed-forward network. Feed-forward networks have the following characteristics: 1. Perceptrons are arranged in layers, with the first layer taking in inputs and the last layer producing outputs. The middle layers have no connection with the external world, and hence are called hidden layers. 2. Each perceptron in one layer is connected to every perceptron on the next layer. Hence information is constantly "fed forward" from one layer to the next., and this explains why these
Physicist John Hopfield showed that a type of recurrent neural network could serve as content-addressable memory, using concepts from statistical physics
Convolutional Neural Networks are great at identifying all the information that makes an image distinct. When we train a deep neural network in Caffe to classify images, we specify a multilayered neural network with different types of layers like convolution, rectified linear unit, softmax loss, and so on. The last layer is the output layer
Why linear regression remains the essential foundation for complex neural networks. Why linear regression remains the essential foundation for complex n
In the prelude, we saw that rule-based systems break when the world gets messy. Traditional machine learning struggles when we cannot describe the world with tidy features. Handwritten digits exposed this clearly.