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3.17.3 Gradient Descent only Converges to Minimizers on Vimeo
"We show that gradient descent generically does not converge to saddle points. This is proved using the Stable Manifold theorem from dynamical systems theory."
talks  optimization  gradient-descent  saddle-point 
august 2017 by arsyed
The Gradient: A Visual Descent
In this post I aim to visually, mathematically and programatically explain the gradient, and how its understanding is crucial for gradient descent.
gradient-descent 
july 2017 by Tafkas
Decoding the Enigma with Recurrent Neural Networks
I am blown away by this -- given that Recurrent Neural Networks are Turing-complete, they can actually automate cryptanalysis given sufficient resources, at least to the degree of simulating the internal workings of the Enigma algorithm given plaintext, ciphertext and key:
The model needed to be very large to capture all the Enigma’s transformations. I had success with a single-celled LSTM model with 3000 hidden units. Training involved about a million steps of batched gradient descent: after a few days on a k40 GPU, I was getting 96-97% accuracy!
machine-learning  deep-learning  rnns  enigma  crypto  cryptanalysis  turing  history  gpus  gradient-descent 
july 2017 by jm

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