If you follow me, you know that this year I started a series called Weekly Digest for Data Science and AI: Python & R, where I highlighted the best libraries, repos, packages, and tools that help us be better data scientists for all kinds of tasks.
The great folks at Heartbeat sponsored a lot of these digests, and they asked me to create a list of the best of the best—those libraries that really changed or improved the way we worked this year (and beyond).
If you want to read the past digests, take a look here:
Disclaimer: This list is based on the libraries and packages I reviewed in my personal newsletter. All of them were trending in one way or another among programmers, data scientists, and AI enthusiasts. Some of them were created before 2018, but if they were trending, they could be considered.
AdaNet is a lightweight and scalable TensorFlow AutoML framework for training and deploying adaptive neural networks using the AdaNet algorithm [Cortes et al. ICML 2017]. AdaNet combines several learned subnetworks in order to mitigate the complexity inherent in designing effective neural networks.
This package will help you selecting optimal neural network architectures, implementing an adaptive algorithm for learning a neural architecture as an ensemble of subnetworks.
You will need to know TensorFlow to use the package because it implements a TensorFlow Estimator, but this will help you simplify your machine learning programming by encapsulating training and also evaluation, prediction and export for serving.
You can build an ensemble of neural networks, and the library will help you optimize an objective that balances the trade-offs between the ensemble’s performance on the training set and its ability to generalize to unseen data.
adanet depends on bug fixes and enhancements not present in TensorFlow releases prior to 1.7. You must install or upgrade your TensorFlow package to at least 1.7:
$ pip install "tensorflow>=1.7.0"
To install from source, you’ll first need to install
bazel following their installation instructions.
cd into its root directory:
$ git clone https://github.com/tensorflow/adanet && cd adanet
adanet root directory run the tests:
$ cd adanet
$ bazel test -c opt //...
Once you have verified that everything works well, install
adanet as a pip package .
You’re now ready to experiment with
Here you can find two examples on the usage of the package:
You can read more about it in the original blog post: