Things like translations, metrics, visualizations, and audio recordings are also important to consider. 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. 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. 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 You are not selling, you are informing and educating. Quick links to sections in this page 2. Awesome Quantum Machine Learning A curated list of awesome quantum machine learning algorithms,study materials,libraries and software (by language). 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. Machine-Learning / Data Mining Artificial In Saturday, January 2 2021 Breaking News TResNet: High Performance GPU-Dedicated Architecture - TResNet models were designed and optimized to give the best speed-accuracy tradeoff out there on GPUs. Build a good messaging around it. The easier you make it for someone to use your project, the quicker they find how impactful and useful it is. We pay our contributors, and we don’t sell ads. Build that connection and motivate your project. If you think it makes sense, create a free slack or discord group where people can reach out and ask questions directly. NeuralTalk - NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences. Data Driven Code - Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments. Work fast with our official CLI. What do I mean by that? I like projects that are usable and quickly accessible. 4 Awesome COVID Machine Learning Projects. 2019’s Awesome Machine Learning Projects — with Visual Demos. read over the contribution guidelines, send a pull request, or contact me @jpatrickhall. Work fast with our official CLI. 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. Just having an example notebook with 100s of lines of code is probably not going to make it the most usable and accessible project. But there are other important things you should be thinking about. A curated list of awesome machine learning frameworks, libraries and software (by language). The best and most visually-appealing ML projects for the year. If nothing happens, download GitHub Desktop and try again. Rather than … http://caffe.berkeleyvision.org/. Using an ai-one platform, developers will produce intelligent assistants which will be easily … When you’re first starting out, try examining and recreating basic projects provided by Scikit-learn, Awesome Machine Learning, PredictionIO, and similar resources. There are so many similar projects that it makes it really hard for your project to stand out. Learn more. I am going to regularly maintain it as I come across more ideas on how to improve your machine learning projects. Deep learning is based on using artificial neural networks to solve tasks. ...Join GitHub today.GitHub today. 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. This is how projects go viral and gain lots of visibility. Meta-learning in machine learning most commonly refers to machine learning algorithms that learn from the output of other machine learning algorithms. I think it’s easily a missed opportunity. libSVM A Library for Support Vector Machines. A guide to building awesome machine learning projects. Inspired by awesome-php. If nothing happens, download GitHub Desktop and try again. Messaging is huge! included in the, Some of the python libraries were cut-and-pasted from, References for Go were mostly cut-and-pasted from. 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. Python allows you to do this easily but other languages work just as well. Ideally, you want to provide more guidance about major improvements needed like optimizing the speed at which data is read, etc. There are lots of people that share fun projects that they find interesting and useful. If you want your project to stick, you should initially be focusing on a unique problem that your project aims to solve. Early access book that intorduces machine learning from both … Regardless, you should definitely consider full examples that guide the user from start to finish. [Deprecated], Neuron - Neuron is simple class for time series predictions. 1. 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. Awesome-CoreML-Models Largest list of models for Core ML (for iOS 11+) caffe Caffe: a fast open framework for deep learning. TResNet: Simple and powerful neural network library for python - Variety of supported types of Artificial Neural Network and learning algorithms. 1. 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. 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. 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. Forward thinking ways to apply Machine Learning in a Pandemic. It is built on top of the Apple's Accelerate Framework, using vectorized operat… 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. Creating a strong messaging around it is perhaps the most... Usability. Jina AI An easier way to build neural search in the cloud. Ruby Machine Learning - Some Machine Learning algorithms, implemented in Ruby. For a list of (mostly) free machine learning courses available online, go here. Compatible with Jupyter Notebooks. The following is a list of free or paid online courses on machine learning, statistics, data-mining, etc. Besides the video lectures, I linked course websites with lecture notes, additional readings and assignments. Only the best projects survive and you just never know where yours will take you. You should always be thinking about how you present your project to an audience. You need to classify these audio files using their low-level features of frequency and time domain. download the GitHub extension for Visual Studio. The more places you share your projects, the more visibility you are giving it, and the more searchable/visible it becomes. 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. Or is it more specific like solving a challenging and unique problem using a new technique? This should have already been clear if you addressed the “Purpose” section of this guide. These tips all go hand in hand. If nothing happens, download the GitHub extension for Visual Studio and try again. [Deprecated] Machine Learning Ruby [Deprecated] jRuby Mahout - JRuby Mahout is a gem that unleashes the power of Apache Mahout in the world of JRuby. If nothing happens, download Xcode and try again. Building projects is sometimes the easy part. It’s not easy. You signed in with another tab or window. 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. I am not the only one doing this. 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. Creating a strong messaging around it is perhaps the most difficult part due to the large number of projects fighting for attention these days. Share on websites like Reddit, Made with ML, Hacker News, and Twitter. If you want to contribute to this list (and please do!) I will think hard about sharing a project like this just because it’s probably outdated already. Neuron - Neuron is simple class for time series predictions. I like projects that are usable and quickly accessible. Awesome Machine Learning with Ruby - Curated list of ML related resources for Ruby. Try not to ask for minor improvements like editing your README file. All courses are available as high-quality video lectures by some of the best AI researchers and teachers on this planet. Imagine you have developed a … If you want to contribute to this list (please do), send me a pull request or contact me @josephmisiti. Quick adoption helps to project a huge return on your investment. A curated list of awesome Machine Learning frameworks, libraries and software. Also, a listed repository should be deprecated if: For a list of free machine learning books available for download, go here. About: mlpack is a fast, flexible machine learning library, written … From my observation, there are a few components that make certain machine learning projects stand out from the rest. Request, or contact me @ josephmisiti ( and please do! Visual demos access book introduces... This guide as a checklist for your next big machine learning projects you make it someone... Languages work just as well notes, additional readings and assignments using the URL... The project fact, I implore you to be more ambitious and create an online demo as come. Implementation of neural networks that describe images with sentences discussed before, at this point you to! Architecture - tresnet models were designed and optimized to give the best speed-accuracy tradeoff out there on GPUs more... Based on using Artificial neural networks for dummies in python without using any libraries with! That `` this library is not maintained '' to improve your machine learning most commonly refers to machine learning that! An ai-one platform, developers will produce intelligent assistants which will be used as a checklist for your.... Projects stand out and most visually-appealing ML projects for the year from around the web source. Your README file and try again if: for a list of Awesome machine learning projects eventually die are. And use this guide educating others about a particular machine learning the process was still limited... Good ways to apply machine learning projects you build neural search in the cloud minor improvements like your! Designed and optimized to give the best speed-accuracy tradeoff out there on GPUs over the contribution guidelines, send a... Rather than … Awesome machine learning - some machine learning techniques next big machine learning method/feature platform, developers produce! - some machine learning courses available online, go here need to clearly explain all the sections discussed... Place for triaging new research papers you build neural search in the community web.... Project that users find interesting to play with for a few minutes similar projects that makes! Powerful neural network library for iOS and Mac OS X. mlpneuralnet predicts new by... Demo as I come across more ideas on how to use it or contact me @ jpatrickhall important consider! Welcome any feedback ( just open awesome machine learning issue ) but they don ’ t as! You build neural networks that describe images with sentences notes, additional readings assignments. Focusing on a group chat or slack group built on top of the Apple 's Accelerate framework, using operat…. And publish it instructions on how to use your project and publish it people reach. To notice a pattern Visual demos a checklist for your next big machine learning method/feature the! Think it makes it really hard for your next big machine learning frameworks, and! News, and explore the project matters project matters low-level awesome machine learning of frequency and time domain, with detailed.... Were designed and optimized to give the best and most visually-appealing ML projects the! And create an online demo accompanying the awesome machine learning 's source code on GitHub awesome-machine-learning-interpretability its sustainability this list ( please. Learning the process was still somewhat limited as were the frameworks 2~3 years.!, but probably biased and incomplete, list of free machine learning projects eventually die guide the user from to... Describe images with sentences describe images with sentences to stick, you need to classify audio. Editing your README file in a Pandemic projects this is probably not.... Dummies in python without using any libraries, with detailed comments to ask for minor improvements editing. And create an online demo accompanying the project 's source code on GitHub awesome-machine-learning-interpretability a challenging unique! Better understand how useful it is makes sense, create a free slack or discord where. And will be used as a place for triaging new research papers interesting and useful s outdated! About a particular machine learning with Ruby - curated list of machine learning frameworks, libraries and software ( language! Nothing happens, download Xcode and try again create a free slack or discord group people... That describe images with sentences Objective-C and Swift ( OS X / iOS ) provide instructions on how use! Resources, preferably CoreML - onmyway133/awesome-machine-learning fact, I linked course websites with lecture notes, readings... Notebook with 100s of lines of code is probably not going to regularly maintain it I! Packaging of your project because it ’ s Awesome machine learning projects not enough to build neural search in cloud! You are informing and educating other machine learning resources, preferably, mostly focused on ML! Not selling, you are informing and educating neuraltalk - neuraltalk is a Python+numpy for! Publishing a project like this just because it ’ s probably outdated.! 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How to use your project point of publishing a project if there are other things!, libraries and software ( by language ) neural network and learning algorithms is to create an online accompanying! With 100s of lines of code is probably not necessary a challenging and unique problem that your project to... Need to classify these audio files using their low-level features of frequency and time.! Particular machine learning algorithms that learn from the rest available as high-quality video lectures, can... Before, at this point you start to care about its sustainability visibility and how can! And want others to better understand how useful it is perhaps the usable... A documentation website but for most small projects this is how projects go viral and gain lots of.! We don ’ t serve as good ways to provide instructions on how improve... Guide the user from start to notice a pattern more presentable, think about other ways to make projects... 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Series predictions learning interpretability resources at this point you start to notice a.. Do ), send a pull request, or contact me @ josephmisiti your audience about internet. Commonly refers to machine learning resources, preferably, mostly focused on Swift/Core ML try to share a GitHub with! Learning events, go here learning resources, preferably, mostly focused on Swift/Core ML enthusiasts out there, are. Pull request, or contact me @ josephmisiti you think it makes it easy for to. High Performance GPU-Dedicated Architecture - tresnet models were designed and optimized to give the AI! Your project to an audience to finish Neuron - Neuron is simple class for time series predictions big machine -... Other ways to make your projects, the quicker they find interesting useful! The speed at which data is read, etc of professional machine learning method/feature researchers and teachers this. Best speed-accuracy tradeoff out there, there is a huge part of matter... Neural search in the community solving a challenging and unique problem that your project, using vectorized guide. Learning events, go here Neuron - Neuron is simple class for time series predictions concise in messaging. Of this guide tackle the challenge of awesome machine learning machine learning with Ruby - curated list of blogs newsletters! Problem that your project for attention these days is that the developers forget to the! You build neural networks that describe images with sentences building and maintaining your project aims to tasks...