cool hit counter Taking stock: the 15 hottest machine learning projects on GitHub this year_Intefrankly

Taking stock: the 15 hottest machine learning projects on GitHub this year

In this article, the author lists the most popular knowledge bases on the GitHub platform in 2017, encompassing various projects in data science, machine learning, and deep learning, which will hopefully help you learn and use them.

GitHub is the most active community in computer science, where people from diverse backgrounds share a growing number of software tools and repositories. In it, you can not only get the tools you need, but also watch how the code is written and implemented.

As a machine learning enthusiast, the author lists the most popular knowledge bases on the GitHub platform in 2017 with learning materials and tools in this article. I hope this will help you in your studies and research.

table of contents

Learning Resources

Awesome Data Science

Machine Learning / Deep Learning Cheat Sheet

Oxford Deep Natural Language Processing Course Lectures

PyTorch – Tutorial

Resources of NIPS 2017

open source tool


TuriCreate – A Simplified Machine Learning Library



Mobile Deep Learning


Deep Photo Style Transfer




Learning Resources

1.Awesome Data Science

Project address.

This repo is a fundamental resource for data science. Countless contributions over the years have built the various resources inside this repo, from getting started guides, to infographics, to accounts you need to follow on social networks. Whether you're a beginner or an industry veteran, there are tons of resources inside to learn.

The depth of the repo can be seen in its table of contents.

2. Machine Learning / Deep Learning Cheat Sheet

Project address.

This project presents tools and techniques commonly used in machine learning/deep learning in the form of a cheatsheet, covering everything from simple tools like pandas to deep learning techniques. After bookmarking or fork the project, you won't have to bother searching for common tips and notes.

As a brief introduction, cheatsheets types include pandas, numpy, scikit learn, matplotlib, ggplot, dplyr, tidyr, pySpark and neural networks.

3. Oxford Deep Natural Language Processing Course Lectures

Project address.

Stanford's NLP course has been the gold standard tutorial in the field of natural language processing. But recently with the development of deep learning, NLP has seen a lot of progress with the help of deep learning architectures like RNN and LSTM.

The repo is based on Oxford University's NLP course and covers advanced techniques and terminology such as language modeling using RNNs, speech recognition, text-to-speech (TTS), and more. This repo contains everything from course material to practical links for the course.

4. PyTorch – Tutorial

Project address.

As of today, PyTorch is still the only competitor to TensorFlow, and its features and reputation make it a competitive deep learning framework. Pytorch has gained a lot of attention from the deep learning community for its Pythonic-style programming, dynamic computational graphs, and faster prototyping.

This knowledge base contains code for a large number of deep learning tasks on PyTorch, including RNN, GAN, and neural style migration. Most of these models require just over 30 lines of code to implement. This speaks volumes about PyTorch's ability to abstract, which allows researchers to focus on finding the right model without getting bogged down in details like programming languages and tool selection.

5. Resources of NIPS 2017

Project address.

This repo contains resources from NIPS 2017 and slides from all invited talks, tutorials and workshops. NIPS is an annual conference on machine learning and computational neuroscience.

Most of the groundbreaking research within the field of data science has appeared as research results at the NIPS conference in the past few years. If you want to be at the forefront of your field, then this is a great resource!

open source software library

1 .TensorFlow

Project address.

TensorFlow is an open source software library that uses data flow graphs for numerical computation. Where Tensor represents the passed data as a tensor (multidimensional array) and Flow represents the computational graph used to perform the operation. Data flow graphs describe mathematical operations in terms of directed graphs consisting of "nodes" and "edges". A "node" is generally used to represent an imposed mathematical operation, but can also represent the beginning of data input and the end of output, or the end of reading/writing persistent variables. The edges represent the input/output relationships between nodes. These data edges can transmit multidimensional arrays of data whose dimensionality can be dynamically adjusted, i.e., tensor.

TensorFlow has maintained its position as the top library for 'deep learning/machine learning' since its official release. The Google Brain team and the machine learning community have also been actively contributing and keeping up to date with the latest developments, especially in the area of deep learning.

TensorFlow started as an open source software library for numerical computation using data flow graphs, but as of now it has become a complete framework for building deep learning models. It currently supports mainly TensorFlow, but also languages such as C, C++ and Java. In addition, this November Google finally released a developer preview version of its new tool, a lightweight solution for TensorFlow for mobile and embedded devices.

2. TuriCreate: a simplified machine learning library

Project address.

TuriCreate is an open source project recently contributed by Apple that provides easy-to-use methods for creating and deploying machine learning models for complex tasks such as target detection, human pose recognition, and recommender systems.

We as machine learning enthusiasts will probably be familiar with GraphLab Create, a very easy and efficient machine learning library, and the company that created it, TuriCreate, caused a big backlash when it was acquired by Apple.

TuriCreate is developed for Python, and its strongest feature is the deployment of machine learning models into Core ML for developing apps for iOS, macOS, watchOS, and tvOS.

3. OpenPose

Project address.

OpenPose is a multi-person keypoint detection library that helps us to detect the position of a person in an image or video in real time. The OpenPose software library, developed and maintained by CMU's Perceptual Computing Lab, is a great case study for illustrating how open source research can be rapidly applied for deployment into industry.

One use case for OpenPose is to help solve the activity detection problem, where an actor's completed movement or activity can be captured in real time. These keypoints and their movements can then be used to create an animated film. Not only does OpenPose have a C++ API to give developers quick access to it, but it also has a simple command line interface for working with images or video.

4. DeepSpeech

Project address.

DeepSpeech is an open source implementation library developed by Baidu, which provides the current top speech-to-text synthesis technology. It's based on TensorFlow and Python, but can also be bound to NodeJS or run using the command line.

Mozilla It has been the construction of DeepSpeech harmony open source software library The main research strength of the,Mozilla Vice President, Technology Strategy Sean White In a blog post, it was written:「 Only a few commercial quality speech recognition engines are currently open source, They are mostly dominated by large companies。 This reduces the number of startups、 Researchers and traditional businesses customize specific products and services for their users。 But we have worked with many developers and researchers in the machine learning community to refine the open source library, So for now DeepSpeech Sophisticated and cutting-edge machine learning techniques have been used to create the speech-to-text engine。」

5. Mobile Deep Learning

Project address.

The repo ports the best current techniques in data science to a mobile platform. The repo was developed by Baidu Research Institute to deploy deep learning models to mobile devices (such as Android and IOS) with low complexity and high speed.

The repo explains a simple use case, namely target detection. It can identify the exact location of a target (e.g. a phone in an image), great isn't it?

6. Visdom

Project address.

Visdom supports the propagation of diagrams, images and text between collaborators. You can organize the visualization space programmatically, or create dashboards for real-time data, examine experiment results, or debug experiment code through the UI.

The inputs in the plot function change, although most of the inputs are a tensor X of data (rather than the data itself) and an (optional) tensor Y (containing optional data variables, such as labels or timestamps). It supports all basic chart types to create Plotly supported visualizations.

Visdom supports the use of PyTorch and Numpy.

7. Deep Photo Style Transfer

Project address.

The repo is based on the recent paper Deep Photo Style Transfer, which introduces a deep learning method for photographic style migration that can process large amounts of image content while efficiently migrating reference styles. The method successfully overcomes distortion and meets the need to migrate photographic styles in a large number of scenarios, including time of day, weather, season, and artistic editing scenarios.


Project address.

CycleGAN is an interesting and powerful library that shows the potential of that top technology. As an example, the following figure roughly demonstrates the library's capability: adjusting the depth of field of an image. The interesting point here is that you don't tell the algorithm beforehand which part of the image to pay attention to. The algorithm did it completely on its own!

The library is currently written in Lua, but it can also be used from the command line.

9. Seq2seq

Project address.

Seq2seq was originally built for machine translation, but has been developed for a variety of other tasks, including summary generation, dialogue modeling, and image capture. The Seq2seq framework can be used as long as a problem is structured to encode input data into one format and decode it into another. It uses all the popular Python based TensorFlow libraries for programming.

10. Pix2code

Project address.

This deep learning project is very exciting in that it tries to automatically generate code for a given GUI. When building a website or mobile device interface, usually front-end engineers have to write a lot of boring code, which is time-consuming and inefficient. This prevents developers from spending major time on implementing real functionality and software logic. The aim of Pix2code is to overcome this difficulty by automating the process. It is based on a new approach that allows to generate computer token with a single GUI screenshot as input.

Pix2code is written in Python and converts captured images from mobile devices and web interfaces into code.

(Source: Heart of the Machine)


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