Picard - "Picard lets you easily declare large spaces of (keras) neural networks and run (hyperopt) optimization experiments on them."

Some Image Augmentation info on Kaggle

Deep learning for complete beginners: neural network fine-tuning techniques by Cambridge Coding Academy

~~Look into using PRELU ELU, etc...~~ Look into MPELU

keras.io/applications/ - Inbuilt pre-trained Models.

keras.io/getting-started/sequential-model-guide/ - Sequential Model Guide. Shows examples!

predict_on_batch(self, x)

fit_generator(self, generator, samples_per_epoch, nb_epoch, verbose=1, callbacks=None, validation_data=None, nb_val_samples=None, class_weight=None, max_q_size=10, nb_worker=1, pickle_safe=False, initial_epoch=0)

Paper with Overview of loss/optimisation functions

Keras Tutorial

Pretrained Models

Visualizing[edit | edit source]

how-convolutional-neural-networks-see-the-world visualizing-deep-neural-networks-classes-and-features

RNN[edit | edit source]

Dimension mismatch in LSTM - Your input should be in this format (sequences, timesteps, dimensions). So based on your example, your input should be in (None, 8, 2). Your input now is (8,2).

Building Autoencoders in Keras - Includes LTSM!!!, Also shows a webserver graph showing progress. How to make a 2d graph. And how to interpolate between numbers...

Help: 'Wrong number of dimensions: expected 3, got 2 with shape (...)

cs231n-CNNs 10 - Recurrent Nerual Networks Lecture

Time Series Prediction with LSTMs

This guys ipython notebook

contextwindow function

Keras Sequence Preprocessing - keras.preprocessing.sequence.pad_sequences

Using Keras LSTM RNN for variable length sequence prediction - Recomends either zero-padding or batches of 1...

Good? - Specifically talks about sliding window. Alice in wonderland.