Archives for vanishing gradient

05 Apr

Yann Lecun and team introduce an efficient method for training deep networks with unitary matrices

Researchers from MIT and Facebook AI have introduced projUNN, an efficient method for training deep networks with unitary matrices.

25 Nov

Can ReLU Cause Exploding Gradients if Applied to Solve Vanishing Gradients?

Vijaysinh Lendave activation functions

The exploding and disappearing gradient problems are the issues that arise when using gradient-based learning methods and backpropagation to train artificial neural networks.

29 Sep

Addressing The Vanishing Gradient Problem: A Guide For Beginners

Yugesh Verma analytics

when the elements of the gradient become exponentially small so that the update of the parameters with the gradient becomes almost insignificant

28 Aug

LSTM Vs GRU in Recurrent Neural Network: A Comparative Study

Vijaysinh Lendave artificial intelligence

Long Short Term Memory in short LSTM is a special kind of RNN capable of learning long term sequences. They were introduced by Schmidhuber and Hochreiter in 1997. It is explicitly designed to avoid long term dependency problems. Remembering the long sequences for a long period of time is its way of working.

The post LSTM Vs GRU in Recurrent Neural Network: A Comparative Study appeared first on Analytics India Magazine.

27 Aug

What Are The Challenges Of Training Recurrent Neural Networks?

Ram Sagar Developers Corner

The traditional feed-forward neural networks are not good with time-series data and other sequences or sequential data. This data can be something as volatile as stock prices or a continuous video stream from an on-board camera of an autonomous car. Handling time series data is where RNNs excel. They were designed to grasp the information…

The post What Are The Challenges Of Training Recurrent Neural Networks? appeared first on Analytics India Magazine.