Skip to main content

30 Amazing Machine Learning Projects for the Past Year (v.2018)

For the past year, we’ve compared nearly 8,800 open source Machine Learning projects to pick Top 30 (0.3% chance).
This is an extremely competitive list and it carefully picks the best open source Machine Learning libraries, datasets and apps published between January and December 2017. Mybridge AI evaluates the quality by considering popularity, engagement and recency. To give you an idea about the quality, the average number of Github stars is 3,558.

Do visit our Hotel price comparison api which compares more than 200 hotel websites to get you the best possible price of your dream hotel.


  • Python Projects of the Year (avg. 3,707 ⭐️): Here (0 duplicate)
  • Learn Machine Learning from Top Articles for the Past Year: Here (0 duplicate)
Open source projects can be useful for data scientists. You can learn by reading the source code and build something on top of the existing projects. Give a plenty of time to play around with Machine Learning projects you may have missed for the past year.
<Recommended Learning>
[68,745 recommends, 4.5/5 stars]
(Click the numbers below. Credit given to the biggest contributor.)

No 1

FastText: Library for fast text representation and classification. [11786 stars on Github]. Courtesy of Facebook Research
……….. [ Muse: Multilingual Unsupervised or Supervised word Embeddings, based on Fast Text. 695 stars on Github]

No 2

Deep-photo-styletransfer: Code and data for paper “Deep Photo Style Transfer” [9747 stars on Github]. Courtesy of Fujun Luan, Ph.D. at Cornell University

No 3

The world’s simplest facial recognition api for Python and the command line [8672 stars on Github]. Courtesy of Adam Geitgey

No 4

Magenta: Music and Art Generation with Machine Intelligence [8113 stars on Github].

No 5

Sonnet: TensorFlow-based neural network library [5731 stars on Github]. Courtesy of Malcolm Reynolds at Deepmind

No 6

deeplearn.js: A hardware-accelerated machine intelligence library for the web [5462 stars on Github]. Courtesy of Nikhil Thorat at Google Brain

No 7

Fast Style Transfer in TensorFlow [4843 stars on Github]. Courtesy of Logan Engstrom at MIT

No 8

Pysc2: StarCraft II Learning Environment [3683 stars on Github]. Courtesy of Timo Ewalds at DeepMind

No 9

AirSim: Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research [3861 stars on Github]. Courtesy of Shital Shah at Microsoft

No 10

Facets: Visualizations for machine learning datasets [3371 stars on Github]. Courtesy of Google Brain

No 11

Style2Paints: AI colorization of images [3310 stars on Github].

No 12

Tensor2Tensor: A library for generalized sequence to sequence models — Google Research [3087 stars on Github]. Courtesy of Ryan Sepassi at Google Brain

No 13

Image-to-image translation in PyTorch (e.g. horse2zebra, edges2cats, and more) [2847 stars on Github]. Courtesy of Jun-Yan Zhu, Ph.D at Berkeley

No 14

Faiss: A library for efficient similarity search and clustering of dense vectors. [2629 stars on Github]. Courtesy of Facebook Research

No 15

Fashion-mnist: A MNIST-like fashion product database [2780 stars on Github]. Courtesy of Han Xiao, Research Scientist Zalando Tech

No 16

ParlAI: A framework for training and evaluating AI models on a variety of openly available dialog datasets [2578 stars on Github]. Courtesy of Alexander Miller at Facebook Research

No 17

Fairseq: Facebook AI Research Sequence-to-Sequence Toolkit [2571 stars on Github].

No 18

Pyro: Deep universal probabilistic programming with Python and PyTorch [2387 stars on Github]. Courtesy of Uber AI Labs

No 19

iGAN: Interactive Image Generation powered by GAN [2369 stars on Github].

No 20

Deep-image-prior: Image restoration with neural networks but without learning [2188 stars on Github]. Courtesy of Dmitry Ulyanov, Ph.D at Skoltech

No 21

Face_classification: Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV. [1967 stars on Github].

No 22

Speech-to-Text-WaveNet : End-to-end sentence level English speech recognition using DeepMind’s WaveNet and tensorflow [1961 stars on Github]. Courtesy of Namju Kim at Kakao Brain

No 23

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation [1954 stars on Github]. Courtesy of Yunjey Choi at Korea University

No 24

Ml-agents: Unity Machine Learning Agents [1658 stars on Github]. Courtesy of Arthur Juliani, Deep Learning at Unity3D

No 25

DeepVideoAnalytics: A distributed visual search and visual data analytics platform [1494 stars on Github]. Courtesy of Akshay Bhat, Ph.D at Cornell University

No 26

OpenNMT: Open-Source Neural Machine Translation in Torch [1490 stars on Github].

No 27

Pix2pixHD: Synthesizing and manipulating 2048x1024 images with conditional GANs [1283 stars on Github]. Courtesy of Ming-Yu Liu at AI Research Scientist at Nvidia

No 28

Horovod: Distributed training framework for TensorFlow. [1188 stars on Github]. Courtesy of Uber Engineering

No 29

AI-Blocks: A powerful and intuitive WYSIWYG interface that allows anyone to create Machine Learning models [899 stars on Github].

No 30

Deep neural networks for voice conversion (voice style transfer) in Tensorflow [845 stars on Github]. Courtesy of Dabi Ahn, AI Research at Kakao Brain

That’s it for Machine Learning open source projects. Do visit our Hotel price comparison api which compares more than 200 hotel websites to get you the best possible price of your dream hotel.


Comments

  1. Thanks for your nice post, Machine LearningMachine Learning is steadily moving away from abstractions and engaging more in business problem solving with support from AI and Deep Learning. With Big Data making its way back to mainstream business activities,For more informations visit Pridesys IT Ltd

    ReplyDelete
  2. Awesome post. Thanks for sharing this post with us.However, a number of the opposite terms do have terribly distinctive meanings. for instance, a man-made neural webwork or neural net could be a Machine learning course system that has been designed to method info in ways in which square measure

    ReplyDelete

Post a Comment

Popular posts from this blog

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Over the past few months, I have been collecting AI cheat sheets. From time to time I share them with friends and colleagues and recently I have been getting asked a lot, so I decided to organize and share the entire collection. To make things more interesting and give context, I added descriptions and/or excerpts for each major topic. This is the most complete list and the Big-O is at the very end, enjoy… If you like this list, you can let me know here Neural Networks

Neural Networks Cheat Sheet Neural Networks Graphs

Neural Networks Graphs Cheat Sheet



Neural Network Cheat Sheet Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
Code Snippets and Github Includedchatbotslife.com
Machine Learning Overview

Machine Learning Cheat Sheet
Machine Learning: Scikit-learn algorithm This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of …

This Is Exactly How You Should Train Yourself To Be Smarter [Infographic]

Design inspired by the Cognitive Bias Codex
View the high resolution version of the infographic by clicking here. Out of all the interventions we can do to make smarter decisions in our life and career, mastering the most useful and universal mental models is arguably the most important. Over the last few months, I’ve written about how many of the most successful self-made billionaire entrepreneurs like Ray Dalio, Elon Musk, and Charlie Munger swear by mental models… “Developing the habit of mastering the multiple models which underlie reality is the best thing you can do. “ — Charlie Munger “Those who understand more of them and understand them well [principles / mental models] know how to interact with the world more effectively than those who know fewer of them or know them less well. “ — Ray Dalio “It is important to view knowledge as sort of a semantic tree — make sure you understand the fundamental principles, i.e. the trunk and big branches, before you get into the leav…

A Tour of The Top 10 Algorithms for Machine Learning Newbies

In machine learning, there’s something called the “No Free Lunch” theorem. In a nutshell, it states that no one algorithm works best for every problem, and it’s especially relevant for supervised learning (i.e. predictive modeling). For example, you can’t say that neural networks are always better than decision trees or vice-versa. There are many factors at play, such as the size and structure of your dataset. As a result, you should try many different algorithms for your problem, while using a hold-out “test set” of data to evaluate performance and select the winner. Of course, the algorithms you try must be appropriate for your problem, which is where picking the right machine learning task comes in. As an analogy, if you need to clean your house, you might use a vacuum, a broom, or a mop, but you wouldn’t bust out a shovel and start digging.
 "Try our Hotel price comparison API to compare more than 200 hotel websites." And if you love this post don't forge…