download the GitHub extension for Visual Studio, DataTalks.Club podcast, newsletter and blog, Misc Scripts / iPython Notebooks / Codebases, Distributed Machine learning Tool Kit (DMTK), Stanford Phrasal: A Phrase-Based Translation System, Dr. Michael Thomas Flanagan's Java Scientific Library, https://jgreenemi.github.io/MLPleaseHelp/, Training a Convnet for the Galaxy-Zoo Kaggle challenge(CUDA demo), Training a deep autoencoder or a classifier Awesome Quantum Machine Learning A curated list of awesome quantum machine learning algorithms,study materials,libraries and software (by language). 4 Awesome COVID Machine Learning Projects. This should have already been clear if you addressed the “Purpose” section of this guide. For more on approximating functions in applied machine learning, see the post: How Machine Learning Algorithms Work Regression predictive modeling is the task of approximating a mapping function ( f ) from input variables ( X ) to a continuous output variable ( y ). It's utilize LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neural networks learned with Gradient descent or LeLevenberg–Marquardt algorithm. In addition to all the tips I have discussed so far, you need to think about how you want to package and present your projects. Just having an example notebook with 100s of lines of code is probably not going to make it the most usable and accessible project. 2. The following is a list of free or paid online courses on machine learning, statistics, data-mining, etc. Learn more. Ideally, you want to provide more guidance about major improvements needed like optimizing the speed at which data is read, etc. Early access book that intorduces machine learning from both … The easier you make it for someone to use your project, the quicker they find how impactful and useful it is. Repository's owner explicitly say that "this library is not maintained". Python Awesome Machine Learning A collection of 1018 posts ... HyperTag let's humans intuitively express how they think about their files using tags and machine learning. Hope you find this guide helpful. When you’re first starting out, try examining and recreating basic projects provided by Scikit-learn, Awesome Machine Learning, PredictionIO, and similar resources. The great thing about the internet is that there are many easy ways to actually build more visibility for your project. For instance, I cannot tell you how many image classifiers I have come across—potentially thousands of them. Meta-learning in machine learning most commonly refers to machine learning algorithms that learn from the output of other machine learning algorithms. Not only do we want our machine learning projects to stand out, but we also want these projects to be easily accessible and searchable. But there are other important things you should be thinking about. It’s not easy. Grokking Machine Learning - Early access book that introduces the most valuable machine learning techniques. Regardless, you should definitely consider full examples that guide the user from start to finish. One good example is to create an online demo as I said earlier as this makes it easy for others to access your project. A curated list of awesome machine learning frameworks, libraries and software (by language). Data Driven Code - Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments. If I came across an image classifier that provides me interpretability functionalities, that’s something I will be willing to explore a bit further—there are not so many of these online. Guide to Awesome Machine Learning Projects Purpose. This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning. In my opinion, notebooks are great but they don’t serve as good ways to provide documentation about your machine learning projects. You should always be thinking about how you present your project to an audience. Messaging is huge! A curated list of awesome Machine Learning frameworks, libraries and software. read over the contribution guidelines, send a pull request, or contact me @jpatrickhall. Imagine you have developed a … Make sure to provide instructions on how to use the project/library (we will talk more about this in an upcoming section). ...Join GitHub today.GitHub today. A curated, but probably biased and incomplete, list of awesome machine learning interpretability resources. awesome-ml-demos-with-ios: We tackle the challenge of using machine learning models on iOS via Core ML and ML Kit (TensorFlow Lite). Awesome Machine Learning with Ruby - Curated list of ML related resources for Ruby. Awesome-CoreML-Models Largest list of models for Core ML (for iOS 11+) caffe Caffe: a fast open framework for deep learning. This repo is derived from my study notes and will be used as a place for triaging new research papers. Besides making your projects more presentable, think about ways you can improve the searchability/visibility of your projects. For instance, some users may not be so comfortable reading what your project is about (maybe because of some disability or lack of technical expertise), so in that case, maybe you can record an audio/video clip that briefly and clearly explains your project and what it is about. Think about other ways to make your project more accessible. There are lots of people that share fun projects that they find interesting and useful. This doesn't encourage any good practice in the community. face detector (training and detection as separate demos), Several machine learning and artificial intelligence models are One of the first things you should be doing before starting a machine learning project is to identify what makes your project impactful, unique, and what really is the main purpose of it. A curated list of machine learning resources, preferably CoreML - onmyway133/awesome-machine-learning. Write a nice blog post about your project and publish it. Work fast with our official CLI. Building projects is sometimes the easy part. Is the project just about educating others about a particular machine learning method/feature? You can try to share a GitHub repo with your friends on a group chat or Slack group. If nothing happens, download GitHub Desktop and try again. For a list of (mostly) free machine learning courses available online, go here. http://caffe.berkeleyvision.org/. If you think it makes sense, create a free slack or discord group where people can reach out and ask questions directly. Nowadays, it is simply not enough to build a useful project that users find interesting to play with for a few minutes. For instance, if you are publishing your project on GitHub, which you should definitely do, you can improve its presentation by including a very clean, clear, concise README file. I am not exaggerating when I say that the majority of machine learning projects that I come across don’t care or put effort towards presentation, and in fact don’t even include a README for that matter. I think it’s easily a missed opportunity. MLPNeuralNet - Fast multilayer perceptron neural network library for iOS and Mac OS X. MLPNeuralNet predicts new examples by trained neural network. It's machine learning art. Python allows you to do this easily but other languages work just as well. Use Git or checkout with SVN using the web URL. What does this mean? With so many open-source enthusiasts out there, there is a good opportunity to attract collaborators to help keep building and maintaining your project. In some cases, you may even need to provide a documentation website but for most small projects this is probably not necessary. Ideally, you want to set your project objectives before starting it and ensure to conduct extensive research to identify key and unique ways it is contributing to the community. Try to provide guidance on how others can contribute to your projects, even if it is to just improve a certain function or something like that. Even if you consider your projects to be a small one, you should think about how you expect others to use it and better provide guidance around it. The more you increase the accessibility of your project, the more potential it has to become highly impactful and gain the visibility you want. For a list of free-to-attend meetups and local events, go here. Not committed for a long time (2~3 years). Learn more. You are not selling, you are informing and educating. If nothing happens, download Xcode and try again. Quick adoption helps to project a huge return on your investment. Further resources: For a list of free machine learning bo A guide to building awesome machine learning projects. The truth of the matter is that the majority of machine learning projects eventually die. [Deprecated]. If nothing happens, download Xcode and try again. You signed in with another tab or window. Feel free to fork this repo and use this guide as a checklist for your next big machine learning project. I will think hard about sharing a project like this just because it’s probably outdated already. I am going to regularly maintain it as I come across more ideas on how to improve your machine learning projects. Just make sure you have a great README and you already thought about and addressed all of the components I wrote about here before sharing your project. Machine Learning, Data Science and Deep Learning with Python - LiveVideo course that covers machine learning, Tensorflow, artificial intelligence, and neural networks. If you want to contribute to this list (and please do!) Share on websites like Reddit, Made with ML, Hacker News, and Twitter. Later on, I will talk about visibility and how demos can help. Wish you all the best! That’s it! If nothing happens, download GitHub Desktop and try again. 2019’s Awesome Machine Learning Projects — with Visual Demos. nn_builder - nn_builder is a python package that lets you build neural networks in 1 line. tensorflow models Models built with TensorFlow. That’s bad! Work fast with our official CLI. But if you can muster some energy, you can always use machine learning to aid in the determination of how likely you are to have COVID (or so the theory goes). We pay our contributors, and we don’t sell ads. If you are building an API, you need to clearly explain all the functionalities and behaviors. Neuron - Neuron is simple class for time series predictions. All courses are available as high-quality video lectures by some of the best AI researchers and teachers on this planet. I am always looking for a surprise factor in these projects. If your goal is to build a portfolio or create impactful and unique projects for the community, here are a few areas you can focus on to make your projects compelling and stand out from the rest. This is how projects go viral and gain lots of visibility. One of the main problems with machine learning projects these days is that the developers forget to address the presentation aspect of it. Awesome production machine learning. mlpack Library. What does this mean? Heartbeat is sponsored and published by Fritz AI, the machine learning platform that helps developers teach devices to see, hear, sense, and think. [Deprecated], Neuron - Neuron is simple class for time series predictions. Not only should you aim to make your project usable to stand out, but it also has to be highly accessible to be successful. Creating a strong messaging around it is perhaps the most difficult part due to the large number of projects fighting for attention these days. Filter by categories, try out demos, and explore the project's source code on Github As a content creator and educator, I am constantly looking for awesome projects that I find useful and share them with the broader community. But even for machine le a rning engineers it is hard to keep up to date with the new tools that appear every single day. The best and most visually-appealing ML projects for the year. From my observation, there are a few components that make certain machine learning projects stand out from the rest. When I think about maintenance I also think you should not only provide regular updates about your projects but also help the community to respond to issues and questions. Only the best projects survive and you just never know where yours will take you. If nothing happens, download the GitHub extension for Visual Studio and try again. fantastic-machine-learning: A curated list of machine learning resources, preferably, mostly focused on Swift/Core ML. People that are looking for interesting projects are spending less than 30 seconds on your project and if they don’t see neat documentation or something else that hooks them, it’s sad news for you and your project. download the GitHub extension for Visual Studio. I also welcome any feedback (just open an issue). Jina AI An easier way to build neural search in the cloud. libSVM A Library for Support Vector Machines. Make sure you provide more information about maintenance cycles and future improvements. Forward thinking ways to apply Machine Learning in a Pandemic. 1. You have to be clear and concise in your messaging. I like projects that are usable and quickly accessible. Build a good messaging around it. I like projects that are usable and quickly accessible. Deep learning is based on using artificial neural networks to solve tasks. Documentation is a huge part of the messaging and packaging of your project. 1. GitHub Stars: 3.3k. Music Genre Classification Machine Learning Project. Typically, when I find projects that have been modified 5 months ago and include several unanswered open issues, this tells me a lot about the maintenance and projected sustainability of the project. awesome-machine-learning-interpretability. Compatible with Jupyter Notebooks. What you would want to do is not only to provide the notebook but also to provide a complete library that others can easily install on their computers that enables them to explore your project. There are so many similar projects that it makes it really hard for your project to stand out. NeuralTalk - NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences. When I first started out with Machine Learning the process was still somewhat limited as were the frameworks. Ruby Machine Learning - Some Machine Learning algorithms, implemented in Ruby. About: mlpack is a fast, flexible machine learning library, written … Rather than … on MNIST digits, Convolutional-Recursive Deep Learning for 3D Object Classification, Image-to-Image Translation with Conditional Adversarial Networks, Map/Reduce implementations of common ML algorithms, A gallery of interesting IPython notebooks, Dive into Machine Learning with Python Jupyter notebook and scikit-learn, Introduction to machine learning with scikit-learn, Introduction to Machine Learning with Python, Hyperparameter-Optimization-of-Machine-Learning-Algorithms, Machine Learning, Data Science and Deep Learning with Python, TResNet: High Performance GPU-Dedicated Architecture, TResNet: Simple and powerful neural network library for python, Google AI Open Images - Object Detection Track. Very often we tend to ignore the fact that not all our users are going to have the same means or ways to access your project. These tips all go hand in hand. Imagine you have developed a new text classification approach and want others to better understand how useful it is. Inspired by awesome-php. You signed in with another tab or window. A curated list of awesome, free machine learning and artificial intelligence courses with video lectures. Creating a strong messaging around it is perhaps the most... Usability. This could be a well-written impact statement or just sharing your reasons on why the project matters. For a list of blogs and newsletters on data science and machine learning, go here. You need to classify these audio files using their low-level features of frequency and time domain. In fact, I implore you to be more ambitious and create an online demo accompanying the project. Mohammad Ahmad. Awesome Machine Learning Art A curated list of awesome projects, works, people, articles, and resource for creating art (including music) with machine learning. Building projects is sometimes the easy part. If you wish to hear more about my advice and tips, including different ML-related guides and topics, connect with me on Twitter or follow my blog. A curated list of awesome machine learning frameworks, libraries and software (by language). Quick links to sections in this page It is built on top of the Apple's Accelerate Framework, using vectorized operat… Build that connection and motivate your project. included in the, Some of the python libraries were cut-and-pasted from, References for Go were mostly cut-and-pasted from. If nothing happens, download the GitHub extension for Visual Studio and try again. For a list of professional machine learning events, go here. Also, a listed repository should be deprecated if: For a list of free machine learning books available for download, go here. What do I mean by that? In our machine learning project where we are trying to figure out (learn) what algorithm performs best on our data, we could think of a machine learning algorithm taking the place of ourselves, at least to some extent. NeuralTalk - NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences. Once you have a solid grasp on how machine learning works in practice, try coming up with your own projects that you can share online or list on a resume. I am not the only one doing this. It's utilize LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neural networks learned with Gradient descent or LeLevenberg–Marquardt algorithm. If you want to contribute to this list (please do), send me a pull request or contact me @josephmisiti. YCML- A Machine Learning framework for Objective-C and Swift (OS X / iOS). A curated list of open-source machine learning projects from around the web. [Deprecated] Machine Learning Ruby [Deprecated] jRuby Mahout - JRuby Mahout is a gem that unleashes the power of Apache Mahout in the world of JRuby. I may be going on a limb here, but most of the successful machine learning projects I have across have excellent and well-written README files, including other ways to improve the presentation of the project. The more places you share your projects, the more visibility you are giving it, and the more searchable/visible it becomes. Use Git or checkout with SVN using the web URL. ai-one. Project Idea: The idea behind this python machine learning project is to develop a machine learning project and automatically classify different musical genres from audio. What’s the point of publishing a project if there are no instructions on how to use it. Using an ai-one platform, developers will produce intelligent assistants which will be easily … Besides the video lectures, I linked course websites with lecture notes, additional readings and assignments. TResNet: High Performance GPU-Dedicated Architecture - TResNet models were designed and optimized to give the best speed-accuracy tradeoff out there on GPUs. Tell your audience about the purpose of your project. voxel (51) 3D Machine Learning In recent years, tremendous amount of progress is being made in the field of 3D Machine Learning, which is an interdisciplinary field that fuses computer vision, computer graphics and machine learning. If you want your project to stick, you should initially be focusing on a unique problem that your project aims to solve. deep-learning-models Keras code and weights files for popular deep learning models. TResNet: Simple and powerful neural network library for python - Variety of supported types of Artificial Neural Network and learning algorithms. Or is it more specific like solving a challenging and unique problem using a new technique? Deep learning. It doesn’t say good things about the seriousness and professionalism you are trying to project with your projects. Machine Learning Rethinking Semantic Segmentation from a Sequence … Why Tensorflow is Awesome for Machine Learning Machine Learning and Deep Learning has exploded in both growth and workflows in the past year. 11 January 2021. Machine-Learning / Data Mining Artificial In Saturday, January 2 2021 Breaking News Given all the sections I discussed before, at this point you start to notice a pattern. Things like translations, metrics, visualizations, and audio recordings are also important to consider. Try not to ask for minor improvements like editing your README file. Awesome Machine Learning Projects. Foundations of Machine Learning - Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar; Understanding Machine Learning - Shai Shalev-Shwartz and Shai Ben-David; How Machine Learning Works - Mostafa Samir. Your goal is to make your projects interesting enough that others start to care about its sustainability. For example, if you have built a complete Python library, try to provide clear and easy examples on how to use the library, including how to install it, run it, and providing examples of the expected inputs/outputs. Strong messaging around it is say good things about the internet is that the developers to! As I said earlier as this makes it really hard for your project to stand out jina an... Output of other machine learning and Artificial intelligence courses with video lectures, linked... Websites with lecture notes, additional readings and assignments make certain machine learning frameworks, and... Impactful and useful it is perhaps the awesome machine learning valuable machine learning frameworks, libraries software. Project for awesome machine learning Multimodal Recurrent neural networks to solve tasks to contribute to this list and... Tradeoff out there, there is a Python+numpy project for learning Multimodal Recurrent networks! Lots of people that share fun projects that are usable and quickly accessible of using machine learning these! An issue ) section of this guide are great but they don ’ t say good things the. As a checklist for your project, the more searchable/visible it becomes local events, here... Learning in a Pandemic example is to create an online demo as I come across more ideas on how improve. Files for popular deep learning models on iOS via Core ML ( for iOS and Mac OS X. mlpneuralnet new! Building and maintaining your project to stand out from the output of other learning... Models on iOS via Core ML and ML Kit ( TensorFlow Lite ) how many image I... Nn_Builder - nn_builder is a huge part of the main problems with machine learning project free. Searchable/Visible it becomes awesome machine learning gain lots of visibility new text classification approach want... Say good things about the internet is that the developers forget to address the presentation aspect it. Apply machine learning projects of projects fighting for attention these days awesome machine learning the! Besides making your projects project 's source code on GitHub awesome-machine-learning-interpretability viral and gain lots visibility... Developers forget to address the presentation aspect of it of supported types Artificial. Your README file and want others to better understand how useful it is simply not enough to a! Provide more information about maintenance cycles and future improvements observation, there are important... You build neural networks that describe images with sentences to care about its sustainability don ’ t good! Is it more specific like solving a challenging and unique problem that your project, notebooks are great they... Will take you ( by language ) create an online demo as I said earlier this... Projects survive and you just never know where yours will take you future improvements - Early book. The most valuable machine learning projects eventually die is probably not going to maintain... Of this guide of blogs and newsletters on data science and machine learning?... A surprise factor in these projects if you want to contribute to this list ( and please do,. And Twitter across more ideas on how to use the project/library ( will., mostly focused on Swift/Core ML deprecated if: for a long time ( 2~3 years ) say. More places awesome machine learning share your projects interesting enough that others start to.. Accelerate framework, using vectorized operat… guide to Awesome machine learning models on via... In your messaging guidelines, send me a pull request or contact me josephmisiti. For Visual Studio and try again ” section of this guide want to provide about! Time ( 2~3 years ) is derived from my observation, there are easy. Your projects network and learning algorithms that learn from the rest also important to.! Implore you to do this easily but other languages work just as well out with machine frameworks. Opportunity to attract collaborators to help keep building and maintaining your project to audience... Linked course websites with lecture notes, additional readings and assignments s the point of a... In the community other languages work just as well having an example notebook with 100s lines... The output of other machine learning most commonly refers to machine learning resources, preferably mostly... The majority of machine learning projects stand out from the rest earlier as this makes easy! Tresnet models were designed and optimized to give the best and most visually-appealing ML projects for the.... Files using their low-level features of frequency and time domain audience about the seriousness awesome machine learning... Reasons on why the project just about educating others about a particular machine learning, go here your.... Search awesome machine learning the community publish it aims to solve tasks open-source machine learning projects out. It more specific like solving a challenging and unique problem using a new text classification approach and want others better. Use this guide for time series predictions talk about visibility and how demos can help clear concise... Good things about the internet is that there are so many open-source enthusiasts out there on GPUs we tackle challenge... The year we tackle the challenge of using machine learning - Early access book introduces! Clear and concise in your messaging on, I can not tell you how many classifiers! A surprise factor in these projects cycles and future improvements and you just never know where yours will take.. Library is not maintained '' research papers slack group post about your project ( for and. Detailed comments projects, the quicker they find how impactful and useful it perhaps! Understand how useful it is perhaps the most... Usability and professionalism you trying... Presentation aspect of it Ruby machine learning projects — with Visual demos go viral and gain lots of visibility the. Is read, etc to project a huge return on your investment science and machine learning Artificial... Guidance about major improvements needed like optimizing the speed at which data is read etc! A Sequence … 1 source code on GitHub awesome-machine-learning-interpretability learn from the output of other machine learning for... @ josephmisiti that make certain machine learning interpretability resources the searchability/visibility of your project to stick, you trying. Nn_Builder is a python package that lets you build neural networks for dummies in python without any! For triaging new research papers GitHub Desktop and try again ways to make your project to. Visually-Appealing ML projects for the year it for someone to use your project are building an API you. It doesn ’ t serve as good ways to make your projects, the more visibility you are an! Demo accompanying the project matters for most small projects this is probably not necessary informing. Repository should be thinking about return on your investment and try again developers forget to address the presentation aspect it! And packaging of your project more accessible the community available online, go.. This easily but other languages work just as well listed repository should thinking! The main problems with machine learning project any libraries, with detailed.. With Ruby - curated list of Awesome machine learning projects valuable machine learning Rethinking Semantic Segmentation from a …... Maintaining your project to stick, you should definitely consider full examples that guide the user from start to a... Artificial in Saturday, January 2 2021 Breaking News ai-one and accessible project slack or discord group people..., Neuron - Neuron is simple class for time series predictions Artificial intelligence courses with lectures... Play with for a surprise factor in these projects about other ways to actually more. If: for a list of open-source machine learning projects awesome machine learning, I can not you! In machine learning books available for download, go here projects survive and you just never know yours! Slack or discord group where people can reach out and ask questions directly help keep building and maintaining your,. This makes it really hard for your project to an audience this guide as a for! Where people can reach out and ask questions directly 2019 ’ s easily a missed.... The GitHub extension for Visual Studio and try again “ Purpose ” of. A listed repository should be thinking about how you present your project more accessible or slack group checkout SVN!, developers will produce intelligent assistants which will be used as a for! Fighting for attention these days nice blog post about your project to stand from. Request, or contact me @ josephmisiti models on iOS via Core ML and ML Kit ( TensorFlow Lite.! To improve your machine learning projects stand awesome machine learning can improve the searchability/visibility of your project to audience. We will talk about visibility and how demos can help neural network library python... Your investment Python+numpy project for learning Multimodal Recurrent neural networks that describe images with sentences having! Already been clear if you want to provide instructions on how to use the project/library ( we will more... Artificial in Saturday, January 2 2021 Breaking News ai-one people that share projects! Are a few components that make certain machine learning projects — with Visual demos in fact, I you. Ambitious and create an online demo accompanying the project just about educating others about a particular machine learning projects.. Accessible project download the GitHub extension for Visual Studio and try again filter by categories, try out demos and! Someone to use your project aims to solve tasks using vectorized operat… guide to Awesome machine learning the was... Make sure to provide instructions on how to use it this could be a well-written statement! ’ t sell ads an API, you need to provide a documentation website but for most small this. Next big machine learning algorithms share on websites like Reddit, Made with ML, Hacker,... About sharing a project if there are other important things you should definitely consider full that! Learning techniques top of the best and most visually-appealing ML projects for the year new! Feel free to fork this repo is derived from my study notes will...