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Trends and developments in Reinforcement Learning London TensorFlow Meetup Pierre H. Richemond Imperial College London ...

vinay prabhu on Twitter: "#Keras->#Theano(with GPU),#Torch,#TensorFlow,#Caffe. Universal suffrage 4 #deeplearning. You don't have to choose!

I am using a random dataset with 14 columns (including label column) and 178 rows. To view how your data looks,use “.head ()” function.

So you are using the GPU! And going from a 15 second computation to a third of a second computation is a great jump in performance indeed!

RandomState(seed) indices = np.arange(arrays[0].shape[0]) if shuffle: rgen.shuffle(indices) for start_idx in range(0, indices.shape[0] - batch_size + 1, ...

... the drawdown periods, that is rather promising and could act as a filter for engaging the signal more efficiently or directing the duration of training ...

Mention that GPU reductions are nondeterministic in docs · Issue #2732 · tensorflow/tensorflow · GitHub

from __future__ import print_function,division import numpy as np #import tensorflow as tfimport data import mlp print ("TEST:") net=mlp.

Operations with the same color are batched together, which lets TensorFlow run them faster. The Embed operation converts words to vector representations.

First Contact With TensorFlow - Jordi Torres - Professor and Researcher at UPC & BSC: Supercomputing for Artificial Intelligence and Deep Learning

I like any simulations - even simple ones. This one is great, because it runs and displays images as it goes along. Even with Jupyter, I have yet to figure ...

Lossy image autoencoders with convolution and deconvolution networks in Tensorflow – Giuseppe Bonaccorso

This dataset is built-in in the TensorFlow. Using TF APIs we can easily load the train and eval/test MNIST data:

But actually TensorFlow is not only for that. It also can be used to write other machine leaning algorithms. On this article, I tried to roughly write kNN ...

With custom Estimators, however, TensorBoard only provides one default log (a graph of loss) plus the information we explicitly tell TensorBoard to log.

A seq2seq model translating from Mandarin to English. At each time step, the encoder takes in one Chinese character and its own previous state (black arrow) ...