Train simple CNN

In this workflow we create a simple Convolutional Neural Network using the DL Python Network Creator. We train this network on our image data using the DL Python Network Learner and finally score it using the DL Python Network Executor. The DL Python Network Learner and Executor can be used to write custom training and execution code using Python.

Fine-tune VGG16

In this workflow we are fine-tuning a VGG1G network, similar to "Fine-tune VGG16 (Python)". However, we won't make use of the DL Python Learner/Executor nodes, rather we use the DL Keras Network Learner and DL Network Executor to train and execute our networks in this workflow.

Fine-tune VGG16

 

Neural Machine Translation

Uses a character level encoder-decoder network of LSTMs.
The encoder network reads the input sentence character by character and summarizes the sentence in its state.
This state is then used as initial state of the decoder network to produce the translated sentence one character at a time.
During prediction, the decoder also recieves its previous output as input to the next time.
For training we use a technique called "teacher forcing" i.e. we feed the actual previous character instead of the previous prediction which greatly benefits the training.

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