The 6 most useful Machine Learning projects of the past year (2018)

Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value. They may not be the most theoretically ground breaking works, but they are applicable and practical.

Fast.ai

The Fast.ai library was written to simplify training fast and accurate neural nets using modern best practices. It abstracts away all of the nitty gritty work that can come with implementing deep neural networks in practice. It’s very easy to use and is designed with a practitioner's application building mindset. Originally created for the students of the Fast.ai course, the library is written on top of the easy to use Pytorch library in a clean and concise way. Their documentation is top notch too.

Detectron

Detectron is Facebook AI’s research platform for object detection and instance segmentation research, written in Caffe

FastText

Another one from Facebook research, the fastText library is designed for text representation and classification. It comes with pre-trained models of word vectors for over 150 languages. Such word vectors can be used for many tasks including text classification, summarisation, and translation

AutoKeras

Auto-Keras is an open source software library for automated machine learning (AutoML). It was developed by DATA Lab at Texas A&M University and community contributors. The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. Auto-Keras provides functions to automatically search for the best architecture and hyperparameters for deep learning models.

Dopamine

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms, created by Google. It aims to be flexible yet easy to use, implementing standard RL algorithms, metrics, and benchmarks.

vid2vid

The vid2vid project is a public Pytorch implementation of Nvidia’s state-of-the-art video-to-video synthesis algorithm. The goal with video-to-video synthesis is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an output photo-realistic video that precisely depicts the content of the source video.

Honourable mentions

• ChatterBot: machine learning for conversational dialog engine and creating chat bots
• Kubeflow: machine learning toolkit for Kubernetes
• imgaug: image augmentation for deep learning
• imbalanced-learn: a python package under scikit learn specifically for tacking imbalanced datasets
• mlflow: open source platform to manage the ML lifecycle, including experimentation, reproducibility and deployment.
• AirSim: simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research

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