When you train a network, now you can select the Adams solver or the RMSProp solver. The new function bilstmLayer creates an RNN layer that can learn bidirectional long-term dependencies between time steps. The doc example "Sequence-to-Sequence Regression Using Deep Learning" shows the estimation of engine's remaining useful life (RUL), formulated as a regression problem using an LSTM network. Regression problems, bidirectional layers with LSTM networks I'll focus mostly on what's in the Neural Network Toolbox, with also some mention of the Image Processing Toolbox and the Parallel Computing Toolbox. In this post, I'll summarize the other new capabilities. I showed one new capability, visualizing activations in DAG networks, in my 26-March-2018 post. As usual (lately, at least), there are many new capabilities related to deep learning. MathWorks shipped our R2018a release last month.
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