Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.

Today we’re going see the Top 10 Open source projects can be useful for programmers.

Hope you find an interesting Machine Learning Open Source project that inspires you.


FAIR’s research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

It is Facebook AI Research’s software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework.

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A tool that utilizes deep learning to recognize and swap faces in pictures and videos.

The project has multiple entry points. You will have to:

  • Gather photos (or use the one provided in the training data provided below)
  • Extract faces from your raw photos
  • Train a model on your photos (or use the one provided in the training data provided below)
  • Convert your sources with the model

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Make huge neural nets fit in memory.

Training very deep neural networks requires a lot of memory. Using the tools in this package, developed jointly by Tim Salimans and Yaroslav Bulatov, you can trade off some of this memory usage with computation to make your model fit into memory more easily. For feed-forward models we were able to fit more than 10x larger models onto our GPU, at only a 20% increase in computation time.

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Explaining the predictions of any machine learning classifier.

This project is about explaining what machine learning classifiers (or models) are doing. At the moment, we support explaining individual predictions for text classifiers or classifiers that act on tables (numpy arrays of numerical or categorical data) or images, with a package called lime (short for local interpretable model-agnostic explanations). Lime is based on the work presented in this paper (bibtex here for citation).

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The DeepMind Control Suite and Control Package.

all domains

This package contains:

  • A set of Python Reinforcement Learning environments powered by the MuJoCo physics engine. See the suitesubdirectory.
  • Libraries that provide Python bindings to the MuJoCo physics engine.

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Experimental paradigms implemented using the Psychlab platform (3D platform for agent-based AI).

Psychlab image2

Psychlab recreates the set-up typically used in human psychology experiments inside the virtual DeepMind Lab environment. This usually consists of a participant sitting in front of a computer monitor using a mouse to respond to the onscreen task. Similarly, our environment allows a virtual subject to perform tasks on a virtual computer monitor, using the direction of its gaze to respond. This allows humans and artificial agents to both take the same tests, minimising experimental differences. It also makes it easier to connect with the existing literature in cognitive psychology and draw insights from it.


A deep learning model for style-specific music generation.

Recent advances in deep neural networks have enabled algorithms to compose music that is comparable to music composed by humans. However, few algorithms allow the user to generate music with tunable parameters. The ability to tune properties of generated music will yield more practical benefits for aiding artists, filmmakers, and composers in their creative tasks.


Experimental Global Optimization Algorithm

Simple vs Bayesian Optimization

Simple is a radically more scalable alternative to Bayesian Optimization. Like Bayesian Optimization, it is highly sample-efficient, converging to the global optimum in as few samples as possible. Unlike Bayesian Optimization, it has a runtime performance of O(log(n)) instead of O(n^3) (or O(n^2) with approximations), as well as a constant factor that is roughly three orders of magnitude smaller.

Simple’s runtime performance, combined with its superior sample efficiency in high dimensions, allows the algorithm to easily scale to problems featuring large numbers of design variables.


AI Labs Neuroevolution Algorithms

In the field of deep learning, deep neural networks (DNNs) with many layers and millions of connections are now trained routinely through stochastic gradient descent (SGD). Many assume that the ability of SGD to efficiently compute gradients is essential to this capability.


Towards Neural Phrase-based Machine Translation


Neural Phrase-based Machine Translation (NPMT) explicitly models the phrase structures in output sequences using Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence modeling method. To mitigate the monotonic alignment requirement of SWAN, we introduce a new layer to perform (soft) local reordering of input sequences. Different from existing neural machine translation (NMT) approaches, NPMT does not use attention-based decoding mechanisms. Instead, it directly outputs phrases in a sequential order and can decode in linear time.


That’s it for Machine Learning Monthly Open Source. If you liked this article, please consider Sharing up for my Machine Learning is Fun! Have fun.

Which Machine Learning Project you like most?

Detectron : FAIR’s research platform for object detection research
FaceSwap : swap faces in pictures and videos
Gradient-checkpointing : Make huge neural nets fit in memory
Lime : Explaining the predictions of any machine learning classifier
Dm_control: The DeepMind Control Suite and Control Package
Psychlab : 3D platform for agent-based AI
Deepj : a deep learning model for style-specific music generation
Simple : Experimental Global Optimization Algorithm
Deep-neuroevolution: Deep Neuroevolution
NPMT: Towards Neural Phrase-based Machine Translation
Please Specify:


By Ponglang Petrung

Administrator and PJ at Kamibit Thailand, Android Developer at CodeGears Co., Ltd. and Android Developer, and iOS Application Developer at Appdever

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