Variational Auto-Encoder - nbviewer
batch_size = 100 n_epochs = 100 n_hiddens_recog = [500] n_hiddens_gen = [500] n_latents = 20 n_layers_recog = len(n_hiddens_recog) n_layers_gen = len(n_hiddens_gen) layers = {} # Recognition model. rec_layer_sizes = [(x_train.shape[1], n_hiddens_recog[0])] rec_layer_sizes += zip(n_hiddens_recog[:-1], n_hiddens_recog[1:]) rec_layer_sizes += [(n_hiddens_recog[-1], n_latents * 2)] for i, (n_incoming,
REINFORCEjs: Gridworld with Dynamic Programming
# About **REINFORCEjs** is a Reinforcement Learning library that implements several common RL algorithms supported with fun web demos, and is currently maintained by [@karpathy](https://twitter.com/karpathy). In particular, the library currently includes: ### Dynamic Programming For solving finite (and not too large), deterministic MDPs. The solver uses standard tabular methods will no bells and w
Documentation for GPML Matlab Code
Documentation for GPML Matlab Code version 4.2 1) What? The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaussian Processes for Machine Learning. It has since grown to allow more likelihood functions, further inference methods and a flexible framework for specifying GPs. Other GP packages can be found here. The code is written by Carl Edward Rasmussen
研究の掃溜ノオト Gaussian Processes on Python
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This is a minimal implementation of training for a language model using long short-term memory (LSTM) neural networks
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publications_posters_presentations_code[Finale's Research Website]