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Combining CNNs and RNNs

Part 1: Implementing a 1D CNN

Fifth Section in a Series of Python Deep Learning Posts

Previous sections:

Combining CNNs and RNNs

Because 1D CNNs process input patches independently, they aren’t sensitive to the order of the timesteps (beyond a local scale, the size of the convolution windows), unlike RNNs. Of course, to recognize…

1D_CNN

Implementing a 1D CNN

Fifth Section in a Series of Python Deep Learning Posts

Previous Sections

Implementing a 1D CNN

In section 2, you learned about convolutional neural networks (CNNs) and how they perform particularly well on computer vision problems, due to their ability to operate convolutionally, extracting features from local input patches and allowing for representation modularity and data…

Stacking Recurrent Layers

Fourth Section in a Series of Python Deep Learning Posts.

Previous Sections

Stacking Recurrent Layers

Because you’re no longer overfitting but seem to have hit a performance bottleneck, you should consider increasing the capacity of the network. Recall the description of the universal machine-learning workflow: it’s generally a good idea to increase the capacity of…

Using Recurrent Dropout to Fight Overfitting

Fourth Section in a Series of Python Deep Learning Posts.

Previous sections:

Using Recurrent Dropout to Fight Overfitting

It’s evident from the training and validation curves that the model is overfitting: the training and validation losses start to diverge considerably after a few epochs. You’re already familiar with a classic technique for fighting this phenomenon: dropout…

Recurrent Baseline

Fourth Section in a Series of Python Deep Learning Posts.

Previous Sections

Additionally, you can check out my series of posts on Apache Spark:

Recurrent Baseline

The first fully connected approach didn’t do well, but that doesn’t mean machine learning isn’t applicable to this problem. The previous approach first flattened the timeseries…

A Machine Learning Approach

Fourth Section in a Series of Python Deep Learning Posts.

Previous Sections

Additionally, you can check out the series of posts on Apache Spark

A Machine Learning Approach

In the same way that it’s useful to establish a common-sense baseline before trying machine-learning approaches, it’s useful to try simple, cheap machine-learning models (such as small, densely…

A Non-Machine-Learning Baseline

Fourth Section in a Series of Python Deep Learning Posts.

Previous Sections

Additionally, you can check out the series of posts on Apache Spark

A Non-Machine-Learning Baseline

Before you start using black-box deep-learning models to solve the temperature — prediction problem, let’s try a simple, common-sense approach. It will serve as a sanity check, and…

Preparing the Data

Fourth Section in a Series of Python Deep Learning Posts.

Previous Sections

Additionally, you can check out the series of posts on Apache Spark

Preparing the Data

The exact formulation of the problem will be as follows: given data going as far back as lookback timesteps (a timestep is 10 minutes) and sampled every steps…

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